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the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description As machine learning systems become
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catalytic performance; • development of an experimental database for machine-learning models. The PhD will be conducted at ICPEES within the ECED group, which specializes in heterogeneous catalysis
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, integrating statistical inference, machine learning, and population genetics. We will develop advanced computational methods to characterize the functioning of T- and B-cell repertoires. The goal is to
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will be responsible for designing and implementing physics-informed machine learning strategies for identifying constitutive laws in granular media. This includes the development of thermodynamically
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the complete event reconstruction and systematic uncertainty estimation pipeline for Super-Kamiokande. Reduction of Super-Kamiokande systematic uncertainties using machine learning, control samples
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will investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable
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integrative approaches at the interface of (epi)genomics, machine learning and physics, addressing key challenges in modern quantitative biology. The successful candidate will be responsible for: • Develop
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high-performance GPU cards will be provided to the PhD student to support the development, training, and implementation of machine learning models. Travel and participation costs for project meetings and
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will be compared with the performances of alternative machine learning-based approaches that will put less emphasis on a priori assumptions. Required Skills We are seeking a candidate with a master's
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with LSS. Qualifications. A PhD in astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics