Research engineer - Machine Learning-Aided Mathematical Optimization (MAMO) - (RE1/RE2)

Job Reference

854_24_CASE_WP_RE1/RE2

Position

Research engineer - Machine Learning-Aided Mathematical Optimization (MAMO) - (RE1/RE2)

Closing Date

Monday, 16 December, 2024

Reference: 854_24_CASE_WP_RE1/RE2

Job title: Research engineer - Machine Learning-Aided Mathematical Optimization (MAMO) - (RE1/RE2)

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 Department of Computer Applications for Science and Engineering (CASE) participates in a 5-year European project focused on advanced Language Technologies. To advance that initiative, we seek a young Researcher to embark in the development of algorithms to make learning machines ultimately capable of rapidly comparing the semantic contents of two texts in any language. This application belongs to the general area of Machine Learning-Aided Mathematical Optimization and Natural Language Processing (NLP). It focuses on how to reduce the computational load of a class of mathematical optimization problems, resorting to surrogate ML models.

Mathematical optimization, aka integer programming (IP or ILP), formulations are convex problems solved by discrete optimization, where the objective is the optimal allocation of given resources (represented by decision variables). We already formulated the pure binary algorithm that solves this type of joint set-packing and set-partitioning problem. That entails exploring the complete feasible solutions space, i.e. the space of all possible pairing combinations between the semantic constituents of A and B, a computationally costly and time consuming endeavor. Our challenge lies in diminishing that cost and time-to-solution. Rather than considering the complete feasible solutions space, we are interested in either reducing the size of the feasible solution space from the very outset of the ILP resolution, or altogether replacing the ILP resolution by an inference produced by a surrogate Machine Learning (ML) model.

Among the Natural Language Processing solutions proposed to compare the semantic contents of two texts are various modalities of similarity computations. They extend from the now superseeded, traditional n-gram based lexical comparison, to the comparison of context-aware, semantic representations, in the form of high dimensional vector embeddings, or semantic encodings, of the constituent syntagmata of texts A and B. Vector embeddings are obtained from learned transformer-based language models (LMs), fine-tuned for Natural Language Understanding (NLU).

The successful candidate will use pre-trained, NLU-optimized, distilled LMs to obtain high dimensional semantic encodings for the semantic constituents (SCs) of textual summaries. SCs are (subject, verb, object) triples, automatically extracted from arbitrary texts by custom pre-processing. In this manner comparing texts A and B becomes equivalent to comparing two sets of vector embeddings in high dimensional space. The optimized pairing of set A embeddings with set B embeddings ultimately yield a bipartite graph where each vertex degree is at most 1 and each edge has the similarity of the paired USCs as attribute.

The successful hiree will focus their research & development on:

(i) establishing a containerized HPC-capable Python workflow, applying a free and open-source ILP solver to our joint set-packing and set-partitioning problem, and following the already formulated ILP constraints, thus establishing a baseline for computation.

(ii) establishing a Reinforced Learning (RL) formulation and/or a Graph Neural Network (GNN) design to train as surrogate models, using the sophisticated Marenostrum 5 research infrastructure and its general purpose (CPU-based) and accelerated (GPU-based) partitions.

(iii) setting up a complete performance evaluation framework and obtaining suitable perrformance metrics.

(iv) developping an easy to deploy containerized Python module, exposing end-points via a REST API, capable of running on GPU, or on multi-CPU hosts.

Successful candidates will benefit from expert training and BSC-CNS staff benefits: international multidisciplinary scientific environment and advanced applied research training. We encourage applications from highly motivated candidates with demonstrated experience and interest in applied research, within the context of major European Projects.

Key Duties

• Develop statistical and algorithmic approaches to design and train and qualify surrogate ML models in the areas of Deep Learning and Reinforced Learning, for applications in NLP and Language Technologies.
• Develop containerized Python modules and packages to be integrated in larger LTs workflows, while improving existing code, to satisfy state-of-the-art, frugal, online computing requirements.
• Actively participate in EU projects (participate in teleconferences, workshops, meetings, and writing deliverables.
• Contribute to disseminate the results in peer-reviewed scientific journals and international conferences.
• Teamwork: interaction with scientists in your group, in the department and in the wider community.
  
  

Requirements

• Education
• Candidates with graduate degree or demonstrable equivalent experience (5+ yrs) in computer science, physical sciences, statistical analysis or data science and/or applied mathematics will be at an advantage.
• An MSc in any of those areas, a combination of those areas, or in closely related areas will be positively valued.
• Essential Knowledge and Professional Experience
• Demonstrable experience in computational methodologies for data analysis, machine or statistical learning is expected.
• A passing knowledge of representation learning in Machine Learning, particularly for the design of neural networks (e.g. graph neural networks) and of reinforced learning approaches. (2+ yrs)
• Passing knowledge of combinatorial optimization, Natural Language Processing and distributed computing is expected.
• Hands-on knowledge and experience in OOP languages (Python, ...)
• Familiarity with coding documentation best practices and standards is expected.
• Additional Knowledge and Professional Experience
• Differential calculus, vector analysis, combinatorial optimization , convexity in optimization problems either through advanced courses or as accumulated, on-the-job experience.
• Knowledge of Unix/Linux environments and any related scripting shell (3+ yrs) using the terminal command line interface (CLI).
• Hands-on knowledge of collaborative and version controlled workflows (Git) is required.
• Fluency in English, written and spoken is essential, any other language is a plus.
• Demostrable familiarity with High Performance Computing (HPC), and in particular parallelization paradigms (e.g. OpenMP, MPI) in both distributed-memory and shared-memory computing contexts will be valued.
• Relevant scientific project experience (1+ yrs), liaising with several external partners in scientific programs, gained through work experience – will be positively valued.
• Competences
• Strong analytical skills.
• Ability to work independently, under strict project deadlines, while interacting with a multidisciplinary group.
• Good oral presentation skills, written communication skills.
  

Conditions

• The position will be located at BSC within the CASE 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, support to the relocation procedures
• Duration: Open-ended contract due to technical and scientific activities linked to the project and budget duration
• Holidays: 23 paid vacation days plus 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: December 2024
  
  

Applications procedure and process

All applications must be made through BSC website and contain:

• A full CV in English including contact details
• A Cover Letter with a statement of interest in English, including two contacts for further references - Applications without this document will not be considered
  
  

In accordance with the OTM-R principles, a gender-balanced recruitment panel is formed for every vacancy at the beginning of the process. After reviewing the content of the applications, the panel will start the interviews, with at least one technical and one administrative interview. A profile questionnaire as well as a technical exercise may be required during the process.

The panel will make a final decision and all candidates who had contacts with them will receive a feedback with details on the acceptance or rejection of their profile.

At BSC we are seeking continuous improvement in our recruitment processes, for any suggestions or feedback/complaints about our Recruitment Processes, please contact [email protected].

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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.

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