The Complex Cyber Infrastructure (CCI) group is part of the Informatics Institute at the University of Amsterdam. CCI focuses on the complexity of man-made systems on all scales. Cyber Infrastructure is rapidly evolving from relatively simple fixed components to programmable and virtualized objects with many degrees of freedom, owned, operated and governed by different entities in multiple administrative domains interacting on the Internet. Harnessing this complexity in a transparent, trust-able way for safe and secure data processing is a major research topic that defines the focus of CCI research. The challenges are addressed by combining methods and results from research into distributed data processing, programmable networks, policy reasoning and normative control, hardware and cryptographic security, and software language engineering.

We live in a highly connected and strongly coupled world, and are surrounded by a large diversity of complex systems. All these systems have one thing in common: they process information. At CSL, we aim to understand this information processing in such dynamic multi-level complex systems. Nature is a Complex System that processes information. Computational Science aims to make the complexity of those systems tractable.

Digital cameras have become ubiquitous in the form of consumer cameras, webcams, mobile phones, and professional cameras. These cameras yield enormous streams of data and provide the means for communication, observation, and interaction. Understanding visual data remains a challenge and it is this challenge addressed by the computer vision (CV) group.

The mission of the CV group is to research on core computer vision technologies and in particular color processing, 3D reconstruction, object recognition, and human-behavior analysis. The aim is to provide theories, representation models and computational methods which are essential for image and video understanding. Research ranges from image processing (filtering, feature extraction, reflection modeling, and photometry), invariants (color, descriptors, scene), image understanding (physics‐based, probabilistic), object recognition (classification and detection) to activity recognition with a focus on human‐behavior (eye tracking, facial expression, head pose, age and gender).

The DILAB is a Human-Computer Interaction research group within the Informatics Institute at the University of Amsterdam.

Our research bridges the gap between the technology-oriented and market-led formulation of the smart agenda with a sociological and psychological understanding of what people need artificial intelligence to be, and how data science might enhance our societies.

There are broad societal issues around agency, trust, and ethics which must be considered in the context of how data is gathered and used. There is also increasing debate around the appropriate role for sensors, algorithms, etc. in our everyday lives. Fortunately, this is leading to a heightened awareness of the relative roles of artificial and human intelligence, with a focus on technologies that can augment human capacity rather than attempting to emulate it.

The predominant research questions we try to answer are ‘How can we ensure that advances in artificial intelligence and data science lead to concomitant advances in human values, dignity, wellbeing, and flourishing? How can interactive digital technologies address the pressing societal challenges of today, and the ones of the future, in ways which lead to real impact?’

The focus is on ‘impact’. In an inherently multidisciplinary endeavour, the Digital Interactions Lab, as a Human-Computer Interaction Lab, is committed to connecting the realm of technology with reflections, concepts, and knowledge established in various domains of social science. It also connects with people and domain experts to ensure impact.

The IRLab Amsterdam is part of the Informatics Institute of the University of Amsterdam. Our research focuses on information retrieval: technology to connect people to information. We work on search engines, on recommender systems, and on conversational assistants. There is a heavy emphasis on data-driven methods, for understanding content, for analyzing and predicting user behavior, and for make sense of context.

We combine fundamental, experimental and applied research, and we do so using a broad range of data: text, images, structured information. We are involved in a large number of projects with other groups, both within and outside academia. Our research is funded by NWO, KNAW, the EU and through a range of public-private partnerships. We are strong believers of great science with great impact and greatly value an entrepreneurial spirit.

The IRLab grew out of the Information and Language Processing Systems (ILPS) group and was founded on September 1, 2020. We are proud to have a large and diverse team, with people from over 15 countries, bringing a rich spectrum of perspectives on the challenges that we choose to research.

The INtelligent Data Engineering Lab is part of the Informatics Institute of the University of Amsterdam. It investigates intelligent systems that support people in their work with data and information from diverse sources. This includes addressing problems related to the preparation, management, integration and reuse of data.

We perform both applied and fundamental research informed by empirical insights into data science practice. Recent topics of interest include: data management for machine learning, data search, data provenance, information integration, automated knowledge base / knowledge graph construction, and data semantics.

Breaking down language barriers with language technology

The Language Technology Lab (LTL) is a research group within the Informatics Institute at the University of Amsterdam. LTL focuses on information access from natural language data. Natural language is, simply put, the way humans communicate with each other in speech and text. The group’s unique angle is that they work on language independent technology.

There are thousands of languages in the world. Analysing and translating all those languages in an automated way, would break down language barriers. The predominant question the group tries to answer: How can we represent meaning of texts and how can that be exploited for applications?

LTL positions itself primarily in the AI research theme, with some links to the Data Science theme of the Informatics Institute.

At the Multimedia Analystics Lab (MULTIX), we research multimedia analytics by developing AI techniques for getting the richest information possible from the data, visualizations, and interactions surpassing human and machine intelligence. We blend multi-modal data in effective interfaces for applications and social impact in public health, forensics and law enforcement, cultural heritage, and data-driven business.

At Multiscale Networked Systems (MNS) we research the following areas:

Programmable networks

We focus on the delivery of secure and sustainable ICT services across multiple domains. Device programmability and virtualization will play in this field an ever-increasing role in designing networks and ICT infrastructures. Our research tackles the new questions that emerge from studying the interplay of these novel capabilities.

Data centric processing

Our research investigates an alternative to the current approach to model complex scientific experiments as workflow of dependent tasks, in this approach scientific data is interlinked though data processing transformations which can be discovered and used to create the data processing workflow and not the way around.

Quality critical distributed computing

We research the performance and service quality required by applications that have very high business value or social impact on advanced IT infrastructures. We focus on novel programming and control models for quality critical systems on programmable infrastructures such as Clouds, Edges, and SDNs using blockchain and AI.

The Parallel Computing Systems (PCS) group at the University of Amsterdam performs research on the design, programming and run-time management of multi-core and multi-processor computer systems. The modeling, analysis and optimization of the extra-functional aspects of these systems, such as performance, power/energy consumption, thermals, reliability but also the degree of productivity to design and program these systems, play a pivotal role in this work.

QurAI is an interfaculty group embedded in the Faculties of Medicine (Department of Biomedical Engineering and Physics) and Science (Institute of Informatics) of the University of Amsterdam. Our mission is to enhance patient care by designing and enabling leading edge AI technologies in healthcare.

In 2020, after more than 15 years expertise in solving clinical challenges in medical image analysis, specially in the fields of radiology, cardiology, neonatology and ophthalmology, Ivana Išgum and Clarisa Sánchez founded qurAI with the ambition to enable groundbreaking AI methodology in patient care. The interfaculty nature of the group was their first step to close the common conceptual, and even physical, gap between curiosity-driven and use-inspired AI research in healthcare, and to cohere top, fundamental AI research and excellent clinical practice in one group.

QurAI is established to develop, validate and clinically integrate socially responsible AI solutions to solve data analysis challenges encountered in different steps of the patient pathway, from prevention and triage, through diagnosis and decision making, to care delivery and management.

Our aims are:

1. Bring fundamental AI research and clinical research closer and facilitate the cross-fertilization of these fields.
2. Facilitate interdisciplinary collaboration across UvA to strengthen research and implementation of socially responsible AI in healthcare.
3. Educate the next generation of AI developers in healthcare and the next generation of doctors that will use AI.
4. Enable responsible use of key resources (data, computational power, algorithms, clinical knowledge, clinical workflows) for the development and translation of healthcare innovations.

At SIAS, we are a team of technologically-aware and socially-engaged people that includes scholars, civil servants, policy-makers, law practitioners, students and interns. We value integrity and inclusivity, transparency and trust, commitment and cooperation. We exemplify these values through our services to academia, business, and civil society.

We develop AI technology that highlights the inequality of opportunity in society and that actively increases the prospect of equality of opportunity in education, well-being, environment, mobility and health. We also serve as an information point for residents and businesses who have questions about new AI technologies and the ethical and inclusive use of them.

The Theory of Computer Science (TCS) Group does research on the theoretical foundations of computer science. The aim is to seek greater understanding of fundamental computational techniques and their inherent limitations. The TCS group forms an integral part of a vibrant community of TCS researchers in Amsterdam.

We make sense of video and images with artificial and human intelligence. The lab studies computer vision, deep learning and cognitive science. We are based at the Informatics Institute of the University of Amsterdam.

The VIS Lab embeds four public-private AI labs. QUVA Lab with Qualcomm, Atlas Lab with TomTom, AIM Lab with the Inception Institute of Artificial Intelligence and POP-AART with the Netherlands Cancer Institute and Elekta Oncology Systems. Spin-off’s from the lab include Kepler Vision Technologies and Ellogon.ai.

There are a total of 6 MSc programmes participating:

MSc Artificial Intelligence in the morning for the AI Thesis Fair, and after lunch the following programmes will participate:

MSc Computational Science (joint degree with the VU, hosted at UvA)

MSc Information Studies – Data Science track and Information Systems Track

MSc Logic

MSc Software Engineering

As of the 2024/2025 Academic Year, our internship program follows the UNL model agreement, ensuring the collaboration between universities and businesses while safeguarding trade secrets and intellectual property rights.  Non-disclosure agreements are not accepted. Internships are limited to the Netherlands, and internship providers have a two-week response period to request the removal of confidential information. Failure to respond allows for publication of the full report. Exceptions can be requested in the internship agreement form.

A detailed description of the new policy can be found  here. You may also find a sample internship agreement form  here.

1. The UNL model internship agreement is established in consultation with the business community.
2. It considers the duty of care and educational task of universities, as well as the competitive position of companies.
3. The agreement is applicable to all higher education institutions in the Netherlands and is exclusively for internships within the country.
4. No additional Non-Disclosure Agreements or other agreements are accepted.
5. Trade secrets and intellectual property rights are adequately protected through the model internship agreement and the national collective labor agreement.
6. There is a two-week response period for the removal of confidential information in the thesis or presentation.
7. If the internship provider does not respond within this period, the internship report may be published in full and the presentation will continue.
8. Exceptions to the agreement, such as longer embargo periods or confidential appendices, must be indicated in the internship agreement form.

For any questions please contact internshipagreement-science[at]uva[dot]nl

All project submissions MUST be in English. If the project requires a Dutch-speaking student then you can state that as part of the project requirements. 

For a detailed description of the project requirements, please visit here.

Please take note of each MSc programme’s thesis requirements and create your project proposal accordingly. The projects are reviewed and approved by the individual program Thesis Coordinators. Any project that does not adhere to the project requirements will be rejected and you will be required to make any requested adjustments.

You can find the detailed guide on how to login and upload projects here.

This is how to login for the first time on the new platform.

Login to the Thesis Fair Platform, using your organisation email address you used for your DataNose account.

As it will be the first time you are using the new platform, you will be required to create a new password. To do this, enter your email address, and select “forgot password”. You will then be redirected to the password reset page.

Please note that if the system tells you that your email is not bound to an account, please try another email address as you may have two separate email addresses that were originally tied to DataNose. If you still experience issues, please contact us at thesisfair-IvI(at)uva(dot)nl.

You will then be sent an email containing a code to reset your password. This email may take between 3-5 minutes to arrive. If you have not received it after 5 minutes please check your spam inbox.

After you have received the new code, please make sure to copy and paste it into the code box on the password reset page. You will then be able to choose a new password – your new password must be at least 8 characters long.

After successfully logging in, you will be able to create projects for events.

This is how to upload projects

Once you login, you will be directed to your organisation’s page. Here you can see your organisation’s name, website, and other details.

To upload a project, click on the Projects tab on the left-hand-side banner.

The project form will ask you to provide:

1. Project name,
2. The project’s contact email address,
3. You will need to enter a contact person per project in the project form. If there is no one specifically tied to the project, then the organisation’s primary contact can be listed. If the project contact does not have an account, you can still fill in the email address and then later create an account for your colleague. They will then need to follow the login instructions from above.
4. The number of students the project can accommodate (optional),
5. Project description,
6. Work environment your organisation offers,
7. The expectations the student should try to meet throughout the project.
8. You will also be asked to select the programmes from which you would like to ask students to consider the project.
9. Research tags
10. You will also be asked to select ‘Research Tags’ that correspond to the topic(s)/research area of the project. The research tags selected should define the area of research of the project. These are a rough guide and if your project does not fit one of these tags then choose the tags that are the closest. You need to choose at least 1 research tag with a maximum of 3.

Once you have completed the form, click the ‘Continue to attendance’ button at the bottom of the page. Here, you will be able to confirm your attendance to the fair(s). If your organisation is submitting multiple projects and is attending the event(s), please mark each of the projects for the Fair(s) you are participating in.

1. If your organisation is only attending the AI Thesis Fair, please be certain to only click on AI Thesis Fair.
2. If your organisation is only attending the AI Thesis Fair, please be certain to only click on General Thesis Fair.
3. If you have chosen MSc AI along with other programmes and you intend to attend both events on 13 October, please be sure to click that you are attending both Fairs.
4. If your organisation is not attending any Fairs and your projects are solely for the Marketplace, please check this option for all submitted projects.

You can repeat this process until you have added all your project proposals. If you have a suitable project for students from both MSc AI and one of the other programmes you can put the project for both. If you wish to duplicate a project, you can select the duplicate button at the top corner of the project description box.

If your organisation wishes to re-use a project from a previous year you can just duplicate the project on the main Projects tab and update it accordingly.

You can find the detailed guide on how to login and upload projects  here.



This is how to upload projects

Once you login, you will be directed to your organisation’s page. Here you can see your organisation’s name, website, and other details.

To upload a project, click on the Projects tab on the left-hand-side banner.

The project form will ask you to provide:

1. Project name,
2. The project’s contact email address,
3. You will need to enter a contact person per project in the project form. If there is no one specifically tied to the project, then the organisation’s primary contact can be listed. If the project contact does not have an account, you can still fill in the email address and then later create an account for your colleague. They will then need to follow the login instructions from above.
4. The number of students the project can accommodate (optional),
5. Project description,
6. Work environment your organisation offers,
7. The expectations the student should try to meet throughout the project.
8. You will also be asked to select the programmes from which you would like to ask students to consider the project.
9. Research tags
10. You will also be asked to select ‘Research Tags’ that correspond to the topic(s)/research area of the project. The research tags selected should define the area of research of the project. These are a rough guide and if your project does not fit one of these tags then choose the tags that are the closest. You need to choose at least 1 research tag with a maximum of 3.

Once you have completed the form, click the ‘Continue to attendance’ button at the bottom of the page. Here, you will be able to confirm your attendance to the fair(s). If your organisation is submitting multiple projects and is attending the event(s), please mark each of the projects for the Fair(s) you are participating in.

1. If your organisation is only attending the AI Thesis Fair, please be certain to only click on AI Thesis Fair.
2. If your organisation is only attending the AI Thesis Fair, please be certain to only click on General Thesis Fair.
3. If you have chosen MSc AI along with other programmes and you intend to attend both events on 13 October, please be sure to click that you are attending both Fairs.
4. If your organisation is not attending any Fairs and your projects are solely for the Marketplace, please check this option for all submitted projects.

You can repeat this process until you have added all your project proposals. If you have a suitable project for students from both MSc AI and one of the other programmes you can put the project for both. If you wish to duplicate a project, you can select the duplicate button at the top corner of the project description box.

If your organisation wishes to re-use a project from a previous year you can just duplicate the project on the main Projects tab and update it accordingly.

Here is a good example of a well-written project proposal.

This is possible and it does not cost money to simply have your projects listed on the Project Marketplace. You will still need an account and follow all of the steps in uploading a project. Once you have completed the project submission form, click the ‘Continue to attendance’ button at the bottom of the page. Here, you will be able to confirm your non-attendance to the fair(s) by checking this option for all submitted projects. If your organisation is submitting multiple projects and none of them are for the event(s), please mark each of the projects as not attending. 

Your uploaded projects will still need to adhere to the Project Requirements for each program and will be reviewed and approved/rejected by our academic Thesis Coordinators.

An Open Project is when you wish to leave the topic free to be discussed between you and a student. Or you wish to see what a student would like to propose to you. This allows for some creativity.

To upload one, follow the same instructions under “How do I upload a project proposal?” and then choose 1 to 3 Research Tags that are applicable. If it is an Open Project for a specific type of student, please title it “Open Project AI” or Open Project Software Engineering”; please ensure to choose the corresponding programme.

Once you login, you will be directed to your organisation’s page. Here you can see your organisation’s name, website, and other details. The Organisation’s tab has a button at the bottom “Create new account”. Click on this and fill in the details for your colleague.

Your colleague will then be sent an email containing instructions on how to set up their account. This email may take between 3-5 minutes to arrive. If they have not received it after 5 minutes, please direct them to check their spam inbox.

To attend the event (either the full day or only one of the sessions), you will need to submit at least 1 project and during the submission process you will be asked to confirm your attendance. On this page please select the Fair option that this project is applicable for. This will register the project for the Fair. Please do this for each project you wish to make available at the event. If you have selected this option for a project(s), you will receive an invoice for your attendance at the event.

These are the options you can choose from on the submission form:

1. If your organisation is only attending the AI Thesis Fair, please be certain to only click on AI Thesis Fair.
2. If your organisation is only attending the AI Thesis Fair, please be certain to only click on General Thesis Fair.
3. If you have chosen MSc AI along with other programmes and you intend to attend both events on 13 October, please be sure to click that you are attending both Fairs.
4. If your organisation is not attending any Fairs and your projects are solely for the Marketplace, please check this option for all submitted projects.

You can repeat this process until you have added all your project proposals. If you have a suitable project for students from both MSc AI and one of the other programmes you can put the project for both. If you wish to duplicate a project from a previous year, you can select the duplicate button at the top corner of the project description box.

Please note that you cannot participate in the fair without payment.

If you do not wish to attend the event and instead only have your projects listed in the Project Marketplace, this is possible and it is free. When submitting your projects, do not click on the Thesis Fair option. Please note that these projects still need to be approved by the Thesis Coordinators in order to be listed.

The fees for each Thesis Fair are tiered based upon the type of organisation. When you register, we will assign the applicable tier. Please note that you cannot participate in the Fair without payment first.

€200 (startups, SMEs, government organisations, and non-profits)

€700 (medium/large companies)

€1500 (large companies)

If you do not wish to attend the event and instead only have your projects listed in the Project Marketplace, this is possible and it is free. Please note that these projects still need to be approved by the Thesis Coordinators in order to be listed.

The Thesis Fair takes place on Tuesday, 15 October 2024.

Registration is open with projects due on Friday, 23 August 2024.

The registration fee is due on Tuesday, 8 October.

Students vote on the projects between 18 – 25 SEPTEMBER.

You will need to respond to our request with the number of attending representatives by NO LATER THAN Monday, 30 September.

We have implemented a significant change for the format of the Thesis Fair. Speeddates are no longer part of the event. While students will still vote for projects, these votes will serve as a measure for you to assess the level of interest in your projects and give you an idea of how many representatives to bring.

Instead of speeddates, organisations’ booths will be grouped together into different areas at the KIT. The students will be rotated through each area throughout the event – allowing for all students to interact with every participating organisation if they so wish.

The AI Thesis Fair (AITF) and the general Thesis Fair (TF) will now take place on the same day, Friday 13 October, offering a comprehensive event for all participants. Each event focuses on specific academic areas, allowing you to attend one or both based on your interest in meeting particular types of students.

If you join us for the full day, a single registration fee covers your participation. However, if you prefer to attend only the morning session with our AI students or solely the afternoon session with other GSI students, please note that the fee remains unchanged.

Day Programme

8:30 – 9:00 AI organisations set-up their booths

8:30 AI students arrive

9:15 – 12:15 AI Thesis Fair

12:15 – 12:45 Lunch for AI Thesis Fair participants

12:45 – 13:45 Morning organisations breakdown booths and leave with AI students. New organisations setup booths and the rest of the students arrive.

13:45 – 14:30 Student and new organisation registration

14:30 – 17:30 General Thesis Fair

17:30 – 18:30 Borrel

This depends upon the program the student is in, your capacity, and internship negotiations with the student. The student may have classes that they need to complete and therefore may not be able to start their internship full-time until their Thesis course officially starts.

1. MSc AI students are available for 6 or 8 months starting in November 2023 or January 2024. 
2. MSc Computational Science students are available for 6 months starting in November 2023.
3. MSc Computer Science students are available for 5 months starting in February 2024.
4. MSc Information Studies (Data Science and Information Systems) are available for 6 months, depending upon their class schedules. Typically, they start part-time in January and then move to full time in April. Please discuss with the students directly.
5. MSc Logic. Students are typically available for 6 months, depending upon their class schedules. Usually, they can start full time in January for full-time for 6 months.
6. MSc Software Engineering are available for 3 – 6 months, depending upon their class schedules. Typically, they can start full time in April for 3 months. Some students may be able to start in January part-time. Please discuss with the students directly.

During the event your representatives inform the students on what the next steps are. Whether it is contact via email, setting up appointments already to meet later, or for students to come for a presentation at your organisation at a later date. The format of follow-up is different for every organisation.

Matching/recruiting the students is between you and the students only. The event itself is a meeting point to help ascertain which students are actively interested and qualified for your project(s) and organisation. 

Project requirements

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description.

Thesis projects for the AI Master should focus on research. Hence the main project requirement is an interesting and novel research question the student can dive into.

The goal of the thesis

The final grade of the thesis project is determined by one thing: the thesis manuscript. The entire thesis project should be geared towards this manuscript.

Examples of successful past thesis projects

Successful examples:

1. Greedy InfoMax for Self-Supervised Representation Learning
2. Deep Learning Methods for Deconvolution in Radio Astronomy
3. Deep Reinforcement Learning for Coordination in Traffic Light Control

What does a good project proposal look like?

A good proposal includes a solid and new research question, an interesting research problem, and relevant context (which includes motivation for the research question and optional references).

What does an MSc student from your programme look like?

An AI MSc student undertaking their thesis will be a 2nd year master’s student. The student has a solid foundation in machine learning theory and application, with specialities ranging from computer vision and natural language processing to reinforcement learning and traditional AI.

Main areas of research

1. Machine learning
2. Computer vision
3. Natural language processing
4. Reinforcement learning
5. AI
6. Information retrieval

Timeline of the thesis

The thesis project is either 6 or 8 months long. Please be mindful of this and submit projects for both lengths if possible. A rough expected timeline is as follows:

1 month: developing and understanding

2-4 months for developing method for developing

2 months: finalising

1 month: writing

Scope of the project

A thesis manuscript should contain relevant analyses, methods, and experiments to support the research question.

Project requirements

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description.

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description. The graduate in Computational Science has a thorough knowledge of modelling and simulation of complex systems, computational methods and techniques, and the application of computational methodologies in application fields (ranging from e.g. physics or biology to medical sciences or engineered systems to complex social systems).

The computational science thesis should include one or more of the following broad aspects:

(i) developing computational models, that implement causal mechanisms to understand and predict the behaviour of any system. This could be any system such as social, technical, engineered, biological, physical etc. or

(ii) computational or mathematical techniques to analyze the behaviour of such models. These include strategies for sensitivity analysis, calibration and validation of models. or

(iii) developing computing techniques (e.g. distributed algorithms) to enable large scale, computational models.

The goal of the thesis

The thesis aims to enable the student to develop more in-depth knowledge, understanding, capabilities, and attitudes in the study programme. A Master of Computational Science thesis should emphasize the scientific and modelling (computational ) aspects of the specific system under investigation. The thesis’s overall goal is to display the knowledge and capability required for independent work as a computational scientist.

Computational models are intended to implement causal processes in a single mechanism, which sets them apart from traditional data analysis techniques (such as machine learning) which are applied directly to available data. Without incorporating the same causal mechanisms either explicitly or implicitly (on which research indeed is taking place), in general, data analysis should be restricted to make inferences or predictions ‘within the data (e.g., clustering, regression, interpolation) or perhaps ‘near extrapolation’ (hypothetical scenarios which are still close to that of the data). The outcome of ‘far extrapolation’, however, requires incorporating the relevant causal processes (since their validity extends beyond any data set), which is by definition the goal of computational modelling.

This distinction can also be seen by comparing the ‘inductive capability’ of the learning algorithms (from past data, one can identify patterns) with the deductive capability of computational (mechanistic) models.

Examples of successful past thesis projects

1. Understanding biomass dynamics in semi-arid ecosystems A computational means to an organic end.
2. Causal Discovery from Spontaneous Targeted Interventions
3. Effects of internal viscosity contrasts in a blood flow model based on immersed boundary lattice Boltzmann methods
4. Optimizing Resource Allocation in Socio-economic Systems with the Minority Game: A Case Study on Electric Cars

What does a good project proposal look like?

A good project proposal should:

1. Clearly highlight the novelty of the proposed research and how it builds on the current state-of-art?
2. Clearly highlights the significance of the research. Why now?
3. Clearly highlight the data availability (if applicable) and how data will be accessed.
4. Computational techniques that will be applied in the research.
5. Planned research outcome.

What does an MSc student from your programme look like?

The programme is oriented to prepare students for entry into a PhD programme in Computational Science or related disciplines, or into research positions outside academia. This is mainly triggered by the strong need in science and society for computationally trained researchers, in academia, industry and business.

A number of core and constrained courses (in the first year) help develop students with an independent and scientific mindset.

1. A CLS student is an expert in modelling and simulation as the third pillar of science and is capable of applying abstract models to understand societal questions. Courses such as Agent-Based Modelling and Complex System Simulation (in addition to teaching knowledge and skills) ask students to develop their own research questions and models by themselves on a diverse set of topics.
2. Students can apply models to Implement and study interventions and what-if scenarios to improve/optimize with respect to a practical application.
3. The student has basic knowledge about different techniques for modelling biochemical reactions, metabolic pathways, regulatory networks and cells (Boolean networks, coupled with ordinary differential equations, examples of partial differential equations, optimization techniques, and cell-based models)
4. Students obtain insight into distributed algorithms – concurrency concepts and are offered a bird’s-eye view of a wide range of algorithms for basic and important challenges in distributed systems

Main areas of research

● Theory of complex systems

● Urban complexity

● Computational Biology

● Computational Finance

A rough timeline of the thesis

The research should be independent, but conducted under the daily supervision of (one or more) staff member(s) and embedded in a scientific project of the host institute, and should aim at a scientific publication in a conference or peer-reviewed journal. The Master thesis (42EC) should include an extensive literature survey (accounting for 6EC) that will normally commence during Block 2 of Year 2. From Block 3 Year 2 a student on the normal schedule will then spend full time on the graduation research (a remaining 36EC). The final scheduling should be discussed with the supervisor of the graduation thesis and typically lasts 7 months.

Scope of the project

The projects are not limited to any systems and context as long as they adhere to the requirements.

Project requirements

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description.

The goal of the thesis

Identify a scientific problem in a computer science-related field

Formulate one or more research questions to guide the project

Design a solution to answer the research question(s) in a limited period of time, implement the solution and critically analyze the results

Collaborate with supervisors and other students and communicate (both orally and in writing) about their progress, the results, and the lessons learned.

Main areas of research

1. Artificial Intelligence
2. Bioinformatics
3. Computer Systems
4. High Performance Distributed Computing
5. Systems and Network Security
6. Massivizing Computer Systems
7. Foundational and Experimental Security
8. Sustainable Digital Society
9. Software and Sustainability
10. User Centric Data Science
11. Decentralized Information Society Engineering
12. Theoretical Computer Science

A rough timeline of the thesis

The thesis should be concluded within 5 months with the majority of the students starting in February.

The MSc Information Studies has two tracks, namely Data Science (DS) and Information Systems (IS). Both investigate the use of particular technology with respect to the origination, collection, classification, analysis, sensemaking, manipulation, storage, retrieval and dissemination of information.

In this context, students in the DS track focus on data, related structures and their algorithmic processing, for the generation of information, and the maintenance of data and information, mainly using machine learning approaches.

Students in the IS track focus on cognitive and social implications for the structures within and the use of information systems. Here the relation between data, information and knowledge is investigated on a systems level, including the design of datasets, interaction environments and underlying system architectures.

Project requirements

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description.

The student has to have finished all courses from the 4 blocks of the programme.

The research project’s objective is to give the student an opportunity to acquire practical experience in quantitative and qualitative scientific research methods and to learn to work independently.

The project requires an implementation that can be validated.

A typical Data Science thesis requires hand-labelled data, sufficient complexity going beyond simple approaches such as linear regression and a proper evaluation setup (cross-validation, using more than 1 dataset, train/test, et cetera). For an Information Systems thesis, it is required to have a proper validation for a quantitative or qualitative study. When interviews are used to get a requirement set, the set must be validated and this will normally be done by means of a design (interface, small algorithmic test, outline of a test framework, et cetera).

A master thesis is defined as “an individually written record of the student’s performed original research or design of a scientific nature”. It is an original, independent piece of work especially composed for this occasion containing the creative ideas of the student. Claims, hypotheses, policy recommendations and design choices need to be supported with arguments based on existing theory or empirical evidence.

The master thesis cannot consist of copied resources (internet, books, and journals), unless properly quoted, and the material has not already been submitted elsewhere (other courses, study programmes, universities) with the aim to receive study credits for this. The master thesis can, however, elaborate on previously submitted work, as long as it is clear which contribution of the student has been submitted for which study programme component.

The goal of the thesis

The focus of the thesis research will be the scientific study of a problem-oriented toward actual research themes in academia and society. The thesis project provides students with first-hand experience in working with established scientists or industry experts for a prolonged period of time.

The learning objectives of the research project comprise, that after completion of the thesis project, the student:

1. is able to formulate a clear research question in the field of information studies and design a plan to answer that question
2. can show state‐of‐the‐art knowledge in the area of the research project based on the relevant literature by applying in a practical situation
3. is able to process the research data and to critically judge the obtained results in relation to the goals and the line of research of which the research project is part
4. is able to describe and critically discuss the above activities in a written report, in which the methodology is accounted for and the original phrasing is substantiated
5. is able to present and discuss the results to a scientific and non-scientific audience
6. is able to function in a professional environment.

Examples of successful past thesis projects

1. Efficient Image Similarity Clustering within Apple Orchards on Edge Devices
2. Echocardiographic Clustering by Machine Learning in Children with Early Surgically Corrected Congenital Heart Disease
3. End-to-End Learning on Multi-Edge Graphs with Graph Convolutional Networks
4. Improving the Precision of the HyperLogLog Algorithm by Introducing a Bias
5. A Business Value Perspective on Metadata Management
6. A Requirements-Driven Redesign of a Terminology Maintenance Process in the Netherlands
7. Carsharing: panacea or everlasting promise: Simulating the effect of car sharing policies on the modal split in Amsterdam

What does an MSc student from your programme look like?

The students have good analytical skills regarding problem identification. They can identify potential solutions to the problem and then find in this solution space the answer that best addresses the problem.

Main areas of research

1. Machine Learning
2. NLP
3. Media analysis
4. Data analytics
5. HCI

As the program is interdisciplinary the students have a broad variety of domains they are knowledgeable in.

A rough timeline of the thesis

The project runs in two parts – a 3-month thesis design (part-time) and a 3-month project (full-time). The usual start date of the actual thesis project is the 1st of April to the 30th of June).

Scope of the project

The student has addressed a feasible problem through a research question that can be answered through quantitative or qualitative methods. As the project time is fixed to 3 months the problem cannot be complex and might address a smaller problem (i.e. the comparison of two machine learning approaches to identify the applicability of one as superior.).

Project requirements

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description.

30EC of work, including the writing of the thesis.

The goal of the thesis

The thesis is a report on a substantial piece of scientific work, usually including a significant amount of original research that clearly demonstrates the student’s capacity to independently conduct research in an interdisciplinary environment.

Examples of successful past thesis projects

1. Communicate and Vote: Collective Truth-tracking in Networks
2. The Classicality of Epistemic Multilateral Logic
3. A Compositional Analysis of Dependence Statements
4. Hyperintensional Logics for Evidence, Knowledge and Belief
5. Algebraic models of type theory

What does a good project proposal look like? What do you want to see included?

To be decided case by case.

What does an MSc student from your programme look like?

Mainly formal-theoretical research skills, background in the theoretical sciences like, e.g., Theoretical Computer Science, Theoretical Linguistics, Mathematics and Philosophy.

Main areas of research

● Theoretical sciences such as:

● Theoretical Computer Science

● Theoretical Linguistics

● Mathematics

● Philosophy

A rough timeline of the thesis

The whole thesis amounts to roughly 5 months of work.

Scope of the project

Depends on the project.

Project requirements

The submitted project must be written in English. If the project requires a Dutch speaker please state that in the project description.

The project requires scientific research contributions. The project should be a mix of theory and practice.

The goal of the thesis

The student should aim to master the scientific research process.

Examples of successful past thesis projects

Measuring the degree of library dependency

What does a good project proposal look like? What do you want to see included?

A clear description of the dataset/infrastructure/lab-like environment/real-life case/application that the company can provide the student access to.

What does an MSc student from your programme look like?

The students from MSc SE are quite heterogenous: different countries, and different previous education; one thing they have in common: software is their second nature.

Main areas of research

1. Software engineering
2. Software testing
3. Software evolution
4. Model-based design
5. DevOps
6. Requirements engineering
7. Software process
8. Microservices
9. Programming languages
10. Cyber-physical systems
11. MLOps
12. DataOps
13. DApps
14. Data engineering

A rough timeline of the thesis

1 month thesis proposal, 3-5 months thesis implementation

Scope of the project

Ideally, publishable research, at least at a workshop level

Important information

We work a lot of the time on co-designing the research proposals. We do not expect external organisations to be able to formulate scientific research questions. But we always find very interesting scientific research problems that come from submitted projects that can be formulated into scientific research questions suitable for our students. So, for SE, the companies are lab-like environments where our students conduct real-life experiments. This is extremely valuable and makes our program attractive and high standard. This means however a lot of work in the weeks after the Thesis Fair when the thesis coordinator sits down with students and helps them identify the research gap in these industry projects.

Of course, this is mostly not the case with projects proposed by our former students that are now working in various companies. They already know what we look for in a project and how to formulate scientific research questions.

Only UvA students starting their thesis in 2024/2025 in the following programmes may participate in the Thesis Fair:

1. MSc Artificial Intelligence (Year 2+)
2. MSc Computational Science (Year 2+)
3. MSc Information Studies (Data Science and Information Systems tracks)
4. MSc Logic
5. MSc Software Engineering

Students must vote for projects, IF YOU DO NOT VOTE FOR PROJECTS YOU WILL NOT BE ALLOWED TO ATTEND. Voting and more information will be presented at the Information Sessions. The Information Sessions are visible on your DataNose schedule.

NO you cannot attend both Fairs as projects presented at each Fair are specific for your MSc program. Additionally external organisations will be looking for students from the programs described by the event.

The Project Marketplace will be published and available for students starting on Tuesday, 17 September.

For any questions regarding the Thesis Fairs please send an email to thesisfair-IvI(at)uva.nl.

You will need to contact your Thesis Coordinator. Each program has a specific page or location (website or Canvas) where the thesis information can be found. For MSc AI you can visit the AI Thesis student website first. If you cannot find your answers there, then contact mscAIthesis-GSI(at)uva.nl. For MSc IS you can visit the IS Thesis student website. The Thesis Coordinators for the rest of the programmes can answer your questions:

MSc Software Engineering: Martin Bor

MSc Computational Science: Debraj Roy

MSc Logic: Maria Aloni

MSc Information Studies – Data Science Track: Maarten Marx

MSc Information Studies – Information Systems: Frank Nack

Here are the main differences between an internal and external project:

1. Internal:
2. Internal projects are from our own researchers in our Research Groups. Introduction videos for our AI Research Groups (AI Thesis Fair) and the ILLC have been created and can be found on this page. Videos for our other Research Groups (Thesis Fair) will be created and posted online as we get closer to the event. We will also have the videos presented in various online lectures and sent in the email invitations.
3. To view only internal UvA projects on the Project Marketplace, you can either sort alphabetically and find all projects from organisations that start with “University of Amsterdam” followed by the Research Group’s name (i.e. University of Amsterdam: ILPS). Or you can filter (on the top left) by Organisation Name and type in “University of Amsterdam”, click “Apply Filter”, and all internal projects will be listed without any external projects visible.
4. You will work directly towards contributing towards ongoing research within the university.
5. This is not an internship experience, more academic and less industry applicable.
6. It is a very good idea to take an internal project if you are interested in going for a PhD, as you will most likely be working closely with researchers, postdocs and PhDs.
7. No stipends.
8. Level of knowledge of the supervisors will be more in depth and these are research projects that the researchers are dedicated their lives to (it is their own research).
9. External:
10. These are more of an internship/experience style project.
11. You have to deliver a thesis but will also have to navigate the professional world (work environment, colleagues, office life, etc.).
12. You will have industry networking opportunities and hands on professional experience.
13. There is a potential monthly stipend (this depends upon the organisation and is not guaranteed).
14. You will not only be working on your thesis but doing work for the external organisation.

This will be presented in the Information Sessions on 19 September.

NO you cannot attend if you did not vote. You need to RSVP for Fair and vote for projects. Please see the deadlines above at the top of this page.

Please have your CV complete and uploaded to the Platform. In order to prepare for the Thesis Fair, we recommend that you do the following:

1. Treat each meeting like a job interview and be prepared to discuss the projects and research areas
2. Wear professional clothes and have a clean/tidy appearance. Even though it is an online event we strongly recommend that your whole outfit is professional, you never know if you will need to stand up or not.
3. Prepare questions and do your research for each organisation you meet with – know what the organisation does and their mission
4. Maintain eye contact if possible
5. Be confident but not cocky, listen to their questions and be sure to answer them
6. Be respectful
7. Tell them which program you are in and how long your thesis is, they will need to know how long they will host you. This is not applicable for our MSc AI students as all attending organisations in the morning will know these details.

Here is what you should do during the meetings:

1. Introduce yourself and state when you would begin and for how long
2. Refer to the representative by name
3. Take notes and create questions referring to what the representative has said
4. Ask your prepared questions and ask open questions
5. Check that your ideas and assumptions of the organisation are correct
6. Check that your ideas and interpretations of the project(s) are correct
7. Agree upon further contact and method of contact

BASIC INFORMATION

1. Clear photo of your face (this helps the representatives to remember you from your meeting)
2. First and surname
3. LinkedIn
4. Github
5. Website
6. Email
7. Mobile number
8. Address
9. Programme name and number of months your thesis will last.

INCLUDE THE FOLLOWING DESCRIPTIONS:

1. Profile: Describe your skills, expertise and ambition in 2-3 sentences each
2. Education: Which schools you have attended
3. Work experience: Short sentences with job description and competencies
4. Chosen set of courses and research areas of interest
5. Internships and volunteering

A detailed description of the new policy can be found  here. You may also find a sample internship agreement form  here.

1. The UNL model internship agreement is established in consultation with the business community.
2. It considers the duty of care and educational task of universities, as well as the competitive position of companies.
3. The agreement is applicable to all higher education institutions in the Netherlands and is exclusively for internships within the country.
4. No additional Non-Disclosure Agreements or other agreements are accepted.
5. Trade secrets and intellectual property rights are adequately protected through the model internship agreement and the national collective labor agreement.
6. There is a two-week response period for the removal of confidential information in the thesis or presentation.
7. If the internship provider does not respond within this period, the internship report may be published in full and the presentation will continue.
8. Exceptions to the agreement, such as longer embargo periods or confidential appendices, must be indicated in the internship agreement form.

For any questions please contact internshipagreement-science[at]uva[dot]nl

After the Thesis Fair, we would recommend that you follow up by:

1. Do you have enough information? If not, then do some research.
2. Email or call the companies of your preference. Use the agreed upon contact method.
3. Make an appointment to meet at their office so you can visit the organisation and see the workspace, experience the atmosphere and meet the employees.

If you cannot attend the Fair you can still view all projects on the Thesis Marketplace on. If you find a project you are interested in, then contact the organisation’s contact person on the project page directly.

If you have already voted and you know you cannot attend the Fair please contact us immediately at thesisfair-IvI(at)uva.nl. If you are ill or cannot attend last minute due to other circumstances, please contact us immediately.

NO, as stated above it is not possible to attend the Thesis Fair without voting. You need to RSVP for Fair and vote for projects.