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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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resources. Key questions addressed in the IDYLLE project are variations in heat flux, lithosphere density, and their impacts on vertical motions, stratigraphy, and H₂ production/storage. IDYLLE addresses
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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an intermediate (methanol or ketene) and an acidic zeotype to selectively convert the intermediate to chemicals or fuels. GaZr oxides combined with H-ZSM5 were reported as highly efficient for the hydrogenation
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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 build
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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-VIS-NIR). Experience in hyperspectral data processing (HMSPL, μXRF, μXAS) and statistical analysis (clustering, machine learning) is preferred. Familiarity with fossilization processes and taphonomic
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using