The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses Mare Nostrum, 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 Euro HPC 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.

The deployment of Artificial Intelligence (AI) based solutions to deliver advanced software functionalities is consolidating as a key competitive factor in several industrial domains. AI solutions are the cornerstone for enabling autonomous operation and decision making along the computing continuum from large servers to small edge devices. Increasingly time-critical functionalities are responsible for processing massive amounts of data coming from multiple sensors in a predictable and timely manner. The complexity of AI-based software functionalities encourages the use of highly modular middleware frameworks such as ROS2, Cyber RT, or Autoware, running on top of general-purpose and/or specialized (real-time) operating systems. Performance and (timing) analyzability are two fundamental (and sometimes conflicting) requirements for this type of system, where extensive guarantees must be provided on the capability to deliver correct results in a timely manner, as dictated by domain-specific Functional Safety (Fu Sa) standards.

The objective of this position is to work in a small team on AI solutions for safety-critical systems, to cover a wide scope of activities to enable the analysis and deployment of cutting-edge AI solutions on specialized middleware/runtimes, on top of both mainstream high-computing platforms and industrially representative embedded devices.

We are looking for a candidate with demonstrated experience to coordinate and lead a small group of engineers in the analysis, tailoring, and re-design of AI models and libraries providing effective software-level solutions for the deployment of performance-efficient and functionally safe AI-based solutions in safety-critical systems. The offered position is meant to cover a wide set of activities to support and optimize the deployment of complex AI functions with respect to several dimensions, including but not limited to performance, timing anlaysability, explainability, and Fu SA compliance.

The candidate will be intensively collaborating in multidisciplinary projects, involving experts in AI, real-time computing, hardware analysis and timing analysis, to adapt, optimize, and instantiate AI models and libraries to meet Fu Sa and timing requirements on concrete AI use-case scenarios. The candidate will also elaborate and deploy efficient methodologies for profiling and debugging AI models’ implementations as a means to capturing Fu SA and timing metrics.
The candidate is expected to combine applied investigation and hands-on implementation covering both software and hardware level aspects, including: the functional and non-functional analysis, porting, and optimization of (possibly tailored) AI models and libraries on relevant hardware and software setups, to meet performance, timing and Fu SA requirements, among others, on top of general purpose and specialized or custom