The Research Intership

The Research Internship (RI) is a course offered during the 2nd year by the Institute for Computing and Information Sciences (ICIS) at Radboud University (RU) and at the Engineering School at the University Pompeu Fabra (UPF).

The goal of the RI is for the student to gain experience in their future work field, in academia or industry. It also allows the student to find out about a prospective work environment and some experience in carrying out a larger individual project as preparation for the Master's Thesis. Depending on the student's interests and Master's specialisation, the student can do the Research Internship internally in one of the research groups at UPF or RU or externally at some company or organisation in the industry or the public sector.

Students can also do a RI in an academic setting outside the ICIS / DTIC, for instance, in a research group in another faculty of UPF or RU or at another university.

Finally, the RI can also be done abroad. In this case, special planning is required. Irrespective of the type of project, the student will always need a supervisor who is an ICIS / DTIC academic staff member (i.e. an assistant, associate, or full professor).

Learning Outcomes

Upon completion of the RI, the student will be able to:

Take part in scientific research in the specific area of the student's master’s specialisation: get acquainted with the subject and state of the art, apply appropriate methods, discuss, contribute to research results, relate the activities to current developments in the field;
Present their activities and research outcomes of the RI;
Reflect on the development of their research skills and the role of research in their future career.

Evaluation

The evaluation will be based on the final report of the RI. The final report is a document describing the RI process, including the outcome and some reflection on what the student has done and learnt. The report should contain information about the activities (problem addressed, relevance, discussion, ethical aspects (if applicable), the results (contributions, the outcome of the research, including pointers to sites with relevant information, e.g., datasets or repositories), and the reflection and evaluation:

What went well/not so well/ according to plan?
What was harder/easier than expected?

Timeline

You must decide whether to enrol in a Research Internship by July 1. If you choose this option, you need to know the location and inform the EMAI Coordination, as all internships require prior approval.

Research Internships offered by professors from the Engineering School at Pompeu Fabra University

Professor	Research Topics
Pablo Arias Martinez	Image and Video Restoration, Self-supervised Learning, Bayesian Estimation, Generative Models, Diffusion Models
Coloma Ballester	Computer Vision, Multimodal scene understanding from video, Unsupervised learning
Bart Bijnens	Cardiovascular Diseases, Clinical Decision Support, Manifold Learning, Multiple Kernel Learning
Miguel Calvo-Fullana	Reinforcement Learning, Constrained Learning, Multi-agent, Connected Autonomy, Optimization
Josep Font-Segura	Constellation Design for Optical Communications, Error Probability in 6G Wireless Communications, DNA-Based Storage, Deep Learning Information Theory, Signal Processing and Information Theory in Machine Learning
Vicenç Gómez	Reinforcement Learning, Automated Planning, Probabilistic Inference, Large Language Models, Social Networks
Gloria Haro	Computer Vision, Multimodal analysis, 3D Reconstruction, Sports Video Analysis
Gergely Neu	Reinforcement Learning, Machine Learning Theory, Sequential Decision making, Online Learning, Statistical Learning Theory
Miquel Oliver	Data Analysis and Prediction, Transport System, Blockchain
Narcis Pares	HCI, Mixed Reality, Embodied Interaction, Learning, Autism, Heritage
Gemma Piella	Medical Image Analysis, Computer Vision for Medical Applications, Explainability, Uncertainty Quantification, Generative models
Manuel Portela Charnejovsky	Privacy-preserving data-mining, Geospatial Data Modelling, Responsible Data Sharing Practices
Javier Segovia Aguas	Automated Planning, Reinforcement Learning, Program Synthesis