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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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research tasks, using both analytical and numerical methods, contributing to numerical validation of analytical predictions where relevant; (b) write up results for publication and present them at group
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use numerical models to interpret and predict mechanical behavior. - Carry out a cross-analysis of experimental, microstructural, and numerical results. - Disseminate scientific results (publications
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predictive models of immune responses. Develop advanced and innovative machine learning methodologies and analyze data. The postdoctoral researcher will join the groups of T. Mora and A. Walczak, whose
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generation, targeted atomistic simulations, structural descriptor extraction, and predictive models. The work will aim to establish links between local pore geometry, structural disorder, sodium adsorption
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microscopy, depending on the candidate's expertise; - analyse quantitative biochemical, biophysical, imaging, and cellular data; - integrate experimental findings with computational predictions generated
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the growth of carbon chains. • Collaborate with astrochemists and observers to validate theoretical predictions. This position will focus on modeling dust production in supernova explosions, a key process for
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Experience1 - 4 Additional Information Eligibility criteria - PhD on steganalysis and/or watermarking - knowledge in image coding and on generative methods - knowledge about python Additional comments NA
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atmospheric models on clusters and supercomputers. Experience with code parallelization, MPI (Message Passing Interface), and performance optimization for large-scale simulations using models such as WRF-Chem
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- 4 Additional Information Eligibility criteria - Evolutionary genomics analyses on large NGS datasets -Knowledge on coding (C ,C++, or java), script writing (python, perl), R software and shell Unix