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Candidates must hold a PhD in Mathematical Engineering or Applied Mathematics (or equivalent) Candidates should have a solid background in numerical methods for differential equations, simulation of stochastic
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datasets Use cutting-edge bioinformatics software and methods, or develop novel tools when appropriate Design and lead your research project, with sufficient guidance and mentoring. Contribute
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inverse problems. The team aims at developing Bayesian computational methods for such (ill-posed) inverse problems and aims both at increasing their validity and at reducing their computational cost. In
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, single nuclei RNA sequencing and spatial transcriptomics datasets Use cutting-edge bioinformatics software and methods, or develop novel tools when appropriate Design and lead your research project, with
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datasets Use cutting-edge bioinformatics software and methods, or develop novel tools when appropriate Design and lead your research project, with sufficient guidance and mentoring. Contribute to mentoring
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/ Deep Learning (particularly Computer Vision or 3D perception) Verification & Validation (V&V) of advanced algorithms, software or systems Formal Methods Safety Engineering / Safety-Critical Systems
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of the research are: AI-assisted Bond Issuance, Causal Methods for Enhanced Market Intelligence, Automated Market Research Summaries via RAG-enabled AI. Successful PhD candidates will extensively explore and
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processing, embedded systems, machine learning, and networked communication. Each PhD position corresponds to a dedicated research topic within the consortium. All doctoral researchers will benefit from joint
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reliability (e.g., through formal methods) Experience in software development will be seen as an advantage. Preference may be given to candidates who develop themes complementary to research currently being
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paradigms Software reliability (e.g., through formal methods) Experience in software development will be seen as an advantage. Preference may be given to candidates who develop themes complementary