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Field
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in this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD (or near completion) in a relevant subject area (including Computer Science, Engineering
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include: · PhD in biology, biochemistry, or other life science or a Master’s degree with at least five years of scientific writing, grant and manuscript writing, clinical study design, or clinical
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for magnitudes between 7 and 8. Research program The project will aim to adapt PEGSGraph (Juhel et al., 2024), a graph neural network we designed for rapid magnitude estimation from PEGS (Hourcade et al., 2025) in
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. This PhD will develop agentic explainable AI methods for nucleotide foundation models. Building on emerging preliminary work in the AI for Bioscience group, the project will create computational methods
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extracting data using advanced Natural Language Processing techniques, a knowledge graph of the Romanian labor market will be created. Through an Agent-based Modeling approach the dynamics of the labor market
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that looks plausible but violates physics and is therefore of limited use for grid planning. As a PhD candidate you will develop physics-informed, domain-constrained generative models for energy-system data
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development Qualifications PhD degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages
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-center clinical datasets and contribute to translating advanced computational methods into clinically relevant applications. This PhD position offers a unique opportunity to work at the intersection
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with GREYC (UMR CNRS 6072), the computer science laboratory of Université de Caen Normandie, particularly in machine learning and graph-based approaches. Depending on the scientific questions addressed
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the safe deployment of AI systems in real-world environments. Current research activities focus on topics such as: Constrained Generative models for graph and tabular data. Security, robustness, and