Position: Barcelona Supercomputing Center (BSC), Spain PostDoc on AI modelization for Edge AI systems - AI4S (R3) Context And Mission 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.
These systems must process massive amounts of data from multiple sensors, ensuring reliable and safe operations.
The complexity of AI-based software requires robust mathematical modeling to meet Functional Safety (FuSa) standards, which dictate stringent requirements for these systems to deliver explainable and reliable results.
The candidate will devise mathematical models to address FuSa compliance.
The candidate will collaborate intensively in multidisciplinary projects, involving experts in AI to meet FuSa requirements for specific AI use-case scenarios.
Additionally, the candidate will develop algorithmic-level solutions for monitoring and diagnostics of Edge-AI systems' decisions.
This position aims to work within a small team to develop AI solutions for safety-critical systems on the edge, focusing on the connection between explainability and causality, taking into account the uncertainty in these explanations, and through the use of probabilistic modeling.
These aspects are crucial for meeting FuSa standards by providing transparency, reliability, and predictability in AI systems.
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: BSC Career Opportunities Key Duties Develop mathematical models for Edge-AI systems focusing on explainability, causality, uncertainty modeling, and probabilistic modeling in AI to enhance the reliability and transparency of AI solutions.
Collaborate with multidisciplinary teams to integrate explainability, causality, and uncertainty modeling techniques into existing AI systems.
Identify FuSa-related metrics and develop mathematical approaches for diagnosis and monitoring based on those metrics.
Lead a small team of junior engineers and students contributing to these tasks.
Requirements Education: Master's Degree in Computer Science, Mathematics, or similar.
A PhD in the area (or being in the last year of the PhD).
Essential Knowledge and Professional Experience: Strong knowledge on AI fundamentals.
Familiarity with Deep Learning frameworks (e.g.
PyTorch, Tensorflow, JAX).
Deep knowledge on Causal Inference fundamentals, particularly on the Causal-Graphs perspective.
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