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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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architecture beyond the CMOS-based scheme and suitable energy-efficient hardware for the unconventional scheme is highly desired for future highly-integrated AI hardware on a chip. The French team at IEMN has
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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 increasingly
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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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, 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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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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theoretical and experimental tools to control the magnon dissipation rate using feedback protocols, thereby enabling the continued development, deployment, and use of quantum computing hardware, in alignment
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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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, synthesizing, and processing sound, and on the other hand, by scientific knowledge regarding the phenomenon of sound. Musical acoustics, computer music, and what is more broadly referred to as music research