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14 Mar 2026 Job Information Organisation/Company CNRS Department Géosciences Environnement Toulouse Research Field Mathematics History » History of science Researcher Profile First Stage Researcher
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approaches available in the field S ufficient scientific and methodological hindsight to understand the level of relevance of research approaches and to be able to combine them in an astute way in the analysis
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criteria A strong theoretical background is required, with expertise in ecological and evolutionary theory, mathematics, computational ecology or physics. Also, it is required previous experience with data
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analysis Large language models or machine learning/predictive modeling for longitudinal data analysis Strong computer programming skills Strong mathematical or statistical skills Ability to work as a part of
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pluripotent stem cell (iPSC)-derived cells. Knowledge of advanced bioinformatics tools for DNA/RNA sequencing data analysis, functional annotation, and data visualization (preferred). Experience with advanced
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financial models. The position will include the analysis of hydropower operation and expansion, optimization and equilibrium, market penetration, and interdependencies. This description documents the general
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ATLAS group with a focus on physics analysis and artificial intelligence/machine learning (AI/ML). The successful candidate will contribute to the group’s broad physics program, which includes precision
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Experience Applicants must have experience writing and modifying scientific code, performing data analysis or simulation workflows, and proficiency in at least one programming language such as Python, C, C
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. The postholder will contribute to the systems vaccinology and systems biology workstream of the programme, performing the analysis and integration of high-dimensional immunological and clinical datasets generated
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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database