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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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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
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fundamental understanding of climate change and its impacts, extending to the development of prototype climate services co-designed by stakeholders and climate modeling experts. The goal is to accelerate
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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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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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), I³ (Inorganic–Isotopy–Imaging), ECOMES (emerging contaminants, speciation, and omics), Surface and Interface Characterisation, and Microbiological Characterisation. Together they enable an end‑to‑end
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temporary collaborators, doctoral students and post-doctoral researchers. This laboratory, dedicated to the study of light-induced processes (photosynthesis and photoperception) and chloroplast biology
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description These mesoporous hybrids will be synthesized using the polyvalence of the NHSG process