Researcher – AI4S – LLMs for Code Parallelization (R3)

Job Description

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 Predictable Parallel Computing group and the High Performance Artificial Intelligence research group (expert in AI) are pushing a collaboration between both, focused on the use of AI models for code generation, and particularly, code parallelization.
This position is intended to work at the intersection of both fields, catching up with the state-of-the-art on code generative models, and extending these towards parallelization and optimization tasksmanagement.

The funding for these actions/fellowships and contracts comes from the European Union Recovery and Resilience Facility – Next Generation, within the framework of the General Invitation by the public business entity Red.es to participate in the talent attraction and retention programs within Investment 4 of Component 19 of the Recovery, Transformation, and Resilience Plan.
For more information, please check: https://www.bsc.es/join-us/excellence-career-opportunities/ai4s

Key Duties

• Contribute to the review of the state-of-the-art for code generation, with a focus on parallelization and code optimization.
• Identify inhouse or external data sources. Lead the data gathering and pre-processing pipelines.
• Conduct the design and training of AI models
• Implement reliable, reproducible and automated training and evaluation methodologies. Integrate with HPC environments.
• Document and disseminate the work done

Requirements

• Education
  • Degree in STEM
  • Master in Computer Science
  • PhD in Machine Learning (2020 or before)
• Essential Knowledge and Professional Experience
  • Experience in RISC-V (1 year minimum)
  • Experience with transformers (1 year minimum)
  • Experience with CUDA (2 year minimum)
  • Publications track (5 papers minimum)
  • Advanced knowledge of C++, Python and Bash.
• Additional Knowledge and Professional Experience
  • Fluent English and Spanish are a must.
• Competences
  • Capacity to work in multidisciplinary environments

Conditions

• The position will be located at BSC within the Computer Sciences Department
• We offer a full-time contract (37.5h/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
• Duration: 4 years
• Holidays: 23 paid vacation days plus 24th and 31st of December per our collective agreement
• Salary: 55.000,00 €
• Additional Expenses Grant: Each fellowship will be associated with a grant for additional expenses, such as IT equipment, travel, training, stays, etc.
• Starting date: asap – the incorporation for this vacancy must be before the 16th of December 2024

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:

1. Curriculum Analysis: Evaluation of previous experience and/or scientific history, degree, training, and other professional information relevant to the position. – 40 points
2. 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.