172 phd-position-in-data-modeling-"Prof"-"Prof" Postdoctoral positions at CNRS in France
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Description -Applications are invited for a postdoctoral position to study the neurocomputational mechanisms of information propagation in social networks. The Neuroeconomics group of Dr Jean-Claude Dreher
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26 Sep 2025 Job Information Organisation/Company CNRS Department Centre de Recherche en Informatique, Signal et Automatique de Lille Research Field Engineering Computer science Mathematics
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Experience1 - 4 Additional Information Eligibility criteria The position requires a PhD in Earth sciences, applied mathematics, or a related field, experience with topological analysis or data science, and
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chemistryYears of Research Experience1 - 4 Additional Information Eligibility criteria The candidate must have a PhD in computational chemistry, ideally in material and/or surface science. Experience in modelling
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The H.E.S.S./CTAO team at LPNHE is involved in analyzing data from the H.E.S.S
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Experience1 - 4 Research FieldPhysicsYears of Research Experience1 - 4 Research FieldTechnologyYears of Research Experience1 - 4 Additional Information Eligibility criteria - Modeling of materials using DFT and
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for the inverse modeling of displacement data (Fukushima et al., 2005). Both approaches will be benchmarked on analytic solutions for fractures (Okubo, 1992) and ellipsoids (Nikkhoo and Rivalta, 2022). They will
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Additional Information Eligibility criteria The candidate should have a PhD in Biology/Evolutionary Biology and skills in bioinformatics and macroevolution (ideally models of phenotypic evolution
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criteria - PhD in linguistics, cognitive science or related discipline (equivalent degree to be discussed) - Experience with EMA or other articulatory data acquisition systems (collection and analysis
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of PhD students and post-docs working on different aspects of seafloor spreading (magmatism, tectonics, hydrothermalism) on a range of time scales, with observational and modeling approaches