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is to investigate which antigen specificities are enriched in cell subpopulations, depending on the underlying neurological disease. The project will use high-throughput data to develop and apply
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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analysis, data modeling, and algorithm development. Experience with environmental analysis or microplastic research is a plus but not required. Strong analytical and problem-solving skills, ability
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of teaching and research, the FSTM seeks to generate and disseminate knowledge and train new generations of responsible citizens in order to better understand, explain and advance society and environment we
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neurophysiological biomarkers with long-term neural activity recording and artificial intelligence algorithms to for conditions such as pain, Parkinson’s disease, sleep disorders, Alzheimer’s disease (AD), and
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research environment focusing on integrating multi-source data and developing novel algorithms to address the challenges posed by global environmental change. You will focus on integrating experiments, field
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern
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will have the opportunity to investigate innovative solutions using machine learning algorithms and predictive modelling techniques in the context of a collaborative project with Goodyear Luxembourg (one
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. The HEXAPIC project aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimizing PIC algorithms