Una candidatura hecha para este puesto de trabajo: un currículum y una carta de presentación adaptados que responden directamente a la oferta.
The Barcelona Supercomputing Center (BSC-CNS) invites a Master student in Machine Learning to contribute to the active inference framework for rare diseases within the Life Sciences Department. The internship takes place in a state-of-the-art HPC environment and offers hands-on experience with biomedical data and AI research.
You will work on knowledge graph integration, representation learning, and explainable discovery, participate in group meetings, and collaborate with senior scientists.
395_26_LS_MLBR_R0
Tuesday, 06 October, 2026
Master Student in Machine learning (R0)
Master Student in Machine learning (R0)
The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, was a founding and hosting member of the former European HPC infrastructure PRACE (Partnership for Advanced Computing in Europe), and is now hosting entity for EuroHPC JU, the Joint Undertaking that leads large-scale investments and HPC provision in Europe. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 1000 staff from 60 countries.
We are particularly interested for this role in the strengths and lived experiences of women and underrepresented groups to help us avoid perpetuating biases and oversights in science and IT research. In instances of equal merit, the incorporation of the under-represented sex will be favoured.
We promote Equity, Diversity and Inclusion, fostering an environment where each and every one of us is appreciated for who we are, regardless of our differences.
If you consider that you do not meet all the requirements, we encourage you to continue applying for the job offer. We value diversity of experiences and skills, and you could bring unique perspectives to our team.
The Machine Learning for Biomedical Research Unit, led by Davide Cirillo, is looking for an intern to contribute to the development of an active inference framework for the study of rare diseases.
The Project Aims To Combine Multimodal Biomedical Knowledge Graphs With Active Inference Agents To Support Reasoning Over Rare Disease Hypotheses, While Keeping Every Decision Interpretable. This Study Will Serve As a Proof Of Concept For Applying Active Inference To Clinical Decision Support In Data-sparse, High-uncertainty Settings. The Intern Will Address The Following Tasks
The successful candidate will join a dynamic research group within the Life Sciences department, which integrates independent senior scientists that work on various aspects of computational biology, ranging from bioinformatics for genomics and proteomics to computational biochemistry and text mining. The researcher will work in a highly sophisticated HPC environment, will have access to systems and computational infrastructures, and will establish collaborations with experts in different areas.
The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will be contacted.
BSC-CNS is committed to the principles of the Code of Conduct for the Recruitment of Researchers of the European Commission and the Open, Transparent and Merit-based Recruitment principles (OTM-R). This is applied for any potential candidate in all our processes, for example by creating gender-balanced recruitment panels and recognizing career breaks etc.
BSC-CNS is an equal opportunity employer committed to diversity and inclusion. We are pleased to consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability or any other basis protected by applicable state or local law.
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