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exciting opportunities for machine learning to address outstanding biological questions. The PhD student to be recruited will be working on the development of machine learning methods for single-cell data
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intestinal duct section. To achieve this, we will address the inverse design problem using physics-informed machine learning that consists of determining the optimal structure and material distribution
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(I3S), Sophia Antipolis Hosting lab: I3S & INRIA UniCA Apply by sending an email directly to the supervisor: emanuele.natale@univ-cotedazur.fr Primary discipline: Machine Learning Secondary discipline
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machine learning. We particularly value depth of knowledge, originality, and the potential for cross-disciplinary innovation. Relevant application areas may include (but are not limited to) natural
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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parameter estimation using Bayesian inference, and/or the exploitation of Machine Learning (ML) based algorithms to reduce false positives caused by human generated interference signals in the observational
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, CNRS, I3S, Sophia-Antipolis, France) Collaboration: Luca Calatroni (Luca.calatroni@unige.it), Machine learning Genoa Center, Italy. Context and Post-doc objectives Conventional optical microscopy
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(formulation, algorithms, applications in structural mechanics), HPC computing, reduced-order modelling, machine learning, Vibrations and structural dynamics, architected materials, Additive manufacturing
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by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as