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) To develop Deep Learning algorithms to significantly speed up probabilistic inference algorithms of current spatial birth-death models 2) To incorporate fossil stratigraphic and spatial information into a new
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goals include optimising convective heat transfer using wall oscillations, relating small-scale turbulence to heat transport, modelling large-scale outer flow effects, and developing low-order heat
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creation of a database for the various pollution sensors with a view to training online (non-embedded) models in the first instance. - Development of a machine learning algorithm based on the study database
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» Algorithms Researcher Profile First Stage Researcher (R1) Country France Application Deadline 27 Nov 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1
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nature of the subject. Strong motivation and a rigorous approach to experimental studies are expected, as well as an interest in developing experimental setups and protocols. Other skills such as fluency
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adapt to its complex environment, an individual can employ multiple strategies. On the one hand, it can draw upon its own knowledge acquired through past experiences to extrapolate the consequences
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, more efficient and sustainable processes. The PhD student will be assigned to the PRIMO (Reaction Process Intensification, Membranes and Optimisation) research area, which develops tools and
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a highly motivated researcher associate (M/F) with strong background in computational structural biology. The successful candidate will be working on developing AI-based computational approaches
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of the Khovanov–Seidel representation in new cases, using Garside-type approaches. Developing extensions of Garside theory to the case of infinitely many atoms may also be of interest and will rely on the study of
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using wall oscillations, relating small-scale turbulence to heat transport, modelling large-scale outer flow effects, and developing low-order heat transfer models. Partnerships with industry will