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heritage, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Sub Nanosecond Imaging
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of expensive evaluations required for prediction and optimization. A classical approach is co-Kriging (Kennedy--O'Hagan), which models the high-fidelity response through an autoregressive Gaussian process (GP
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biology, biological data science, multi-omics, imaging, modelling, and AI-driven approaches PhD candidates will benefit from: Access to state-of-the-art experimental and computational platforms Strong
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conductivity, magnetic, etc.). The approach will combine innovative synthesis methods (a new synthesis bench using aerodynamic levitation and laser heating), powder crystallography, high-resolution imaging by
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disease, using magnetic resonance imaging. We also described the molecular profile of soluble mediators secreted by mesenteric adipose tissue—including TNF-α, IL-6, leptin, resistin, and ghrelin—and
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to carry out the following tasks: Development of analysis scripts for the preprocessing and automated processing of functional neuroimaging data; Statistical modeling of imaging data and evaluation
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and obsessive-compulsive disorders), and to optimise neuromodulation therapies such as deep brain stimulation. The team combines intracranial recordings and EEG, brain imaging, brain stimulation
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and economy that respect people and their environment. We are looking for our future postdoctoral researcher in optimization for statistical learning to join the Image, Data, Signal (IDS) department
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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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the technological hurdles at the interface of these disciplines to maximize device sensitivity and deliver a functional prototype, fully validated for transfer to industrial applications. You will be