PhD Student in Machine Learning for Biomedical Research

Job Reference

238_26_LS_MLBR_R1

Position

PhD student in Machine learning (R1)

Closing Date

Wednesday, 27 May, 2026

Reference: 238_26_LS_MLBR_R1

Job title: PhD student in Machine learning (R1)

About BSC

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.

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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.

Context And Mission

The Machine Learning for Biomedical Research unit, led by Davide Cirillo, PhD, is looking for a PhD Student for the development of artificial intelligence applications in cancer research to work on latent representation learning with special emphasis on deep model architectures such as variational autoencoders (VAEs). The work will focus on adult and childhood cancer studies, including the available data from ongoing projects such as IPC (GA 826121). The project aims to develop novel approaches that exploit latent representations of cancer multi-omics data to enable biological and translational studies.

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 and bioinformatics. The candidate 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 work will be carried out at the Machine Learning for Biomedical Research Unit in collaboration with the Computational Biology group, led by Prof. Alfonso Valencia, at BSC Life Sciences Department. The specific candidate’s tasks will involve the development and application of deep learning models for multi-omics cancer data latent representation for generative and discriminative tasks. As such, this job position will be in close relationship with researchers and research projects in a highly-specialized academic environment.

Key Duties

• Deep learning model implementation and deployment
• Biomedical data mining and analysis
• Technical contribution to computational cancer research
• HPC solutions for machine learning applications in life sciences
  
  

Requirements

• Education
• BSc or MSc in Bioinformatics, Computer Science, Biomedical Engineering, Physics, or similar
• Additional background in Cancer or Human Biology, or similar
• Essential Knowledge and Professional Experience
• Programming languages: Python, R, Javascript
• Experience working with machine learning and deep learning frameworks (PyTorch, Tensorflow, Keras)
• Experience working with Unix/Linux systems
• Basic Developer skills including version controls systems and testing
• Additional Knowledge and Professional Experience
• Data analysis and visualization

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• Machine learning
• Statistics
• Spoken and written English.
• Competences
• Ability to understand technical literature and software documentation
• Ability to communicate scientific results
• Ability to work in teams and independently
• Ability to interact with International partners
• Used to work under pressure under strict deadlines
  

Conditions

• The position will be located at BSC within the Life Sciences Department
• We offer a full-time contract (35h/week), a good working environment, a highly stimulating environment with state-of-the-art infrastructure, flexible working hours, extensive training plan, restaurant tickets, private health insurance, support to the relocation procedures
• Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration
• Holidays: 22 days of holidays + 6 personal days + 24th and 31st of December per our collective agreement
• Salary: we offer a competitive salary commensurate with the qualifications and experience of the candidate and according to the cost of living in Barcelona
• Starting date: 01/08/2026
  
  

Applications procedure and process

All applications must be submitted via the BSC website and contain:

• A full CV in English including contact details
• A cover/motivation letter with a statement of interest in English, clearly specifying for which specific area and topics the applicant wishes to be considered. Additionally, two references for further contacts must be included. Applications without this document will not be considered.
  
  

Development of the recruitment process

The selection will be carried out through a competitive examination system ("Concurso-Oposición"). The recruitment process consists of two phases:

• Curriculum Analysis: Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position. - 40 points
• Interview phase: The highest-rated candidates at the curriculum level will be invited to the interview phase, conducted by the corresponding department and Human Resources. In this phase, technical competencies, knowledge, skills, and professional experience related to the position, as well as the required personal competencies, will be evaluated. - 60 points. A minimum of 30 points out of 60 must be obtained to be eligible for the position.
  
  

The recruitment panel will be composed of at least three people, ensuring at least 25% representation of women.

In accordance with OTM-R principles, a gender-balanced recruitment panel is formed for each vacancy at the beginning of the process. After reviewing the content of the applications, the panel will begin the interviews, with at least one technical and one administrative interview. At a minimum, a personality questionnaire as well as a technical exercise will be conducted during the process.

The panel will make a final decision, and all individuals who participated in the interview phase will receive feedback with details on the acceptance or rejection of their profile.

At BSC, we seek continuous improvement in our recruitment processes. For any suggestions or comments/complaints about our recruitment processes, please contact recruitment@bsc.es.

For more information, please follow this link.

Deadline

The vacancy will remain open until a suitable candidate has been hired. Applications will be regularly reviewed and potential candidates will be contacted.

OTM-R principles for selection processes

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.

For more information follow this link

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