Research Engineer

**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 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.**Context And Mission**

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 hiree will focus their research & development on:
(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.**Key Duties**
- 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
- 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 ac