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2 Apr 2026 Job Information Organisation/Company École Normale Supérieure Department Physics Research Field Physics » Statistical physics Researcher Profile First Stage Researcher (R1) Positions
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interdisciplinary research team. PhD in one of the following areas: infectious disease epidemiology, mathematics, statistics, physics, AI, computer science, population biology or a similarly quantitative discipline
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Candidate Profile Training and Skills required (Recent) PhD in bioinformatics, statistics, or computer science with knowledge and interest in biology Track record of creativity in developing analytic
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, clinical and histological data in a translational framework. Main activities: - Bioinformatic analysis of WES and RNA-seq data. - Somatic variant detection and annotation. - Statistical and clinical
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. Starting position for grantees will be March-April 2027. PhD candidates are welcome to apply but they must obtain their PhD degree by the end of 2026. Fellowship is for a duration of 24 to 36 months
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. - Integration of multi-omics datasets (genomics, transcriptomics, proteomics). - Statistical modeling and survival analysis. - Collaboration with AI specialists for histology-based prediction models
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new pollen analysis • Modeling and Statistics: Utilize GIS, R statistical environment, and other tools for pollen-based modeling (REVEALS and LOVE models) and statistical analysis. • Collaboration: Work
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SD-26085 POSTDOCTORAL RESEARCHER IN THE OXIDATIVE CHEMICAL VAPOR DEPOSITION OF FUNCTIONAL BUILDIN...
in December 2027). Is Your profile described below? Are you our future colleague? Apply now! Education · The candidate should have a PhD degree in Chemistry, Chemical Engineering, Materials
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, probability, and statistical learning. You must also be proficient in Python and speak English. You have a PhD or equivalent degree and are able to work in a team, communicate effectively, and write clear
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empirically the historical evolution of political cleavages and socioeconomic inequalities, with a multidisciplinary perspective and the use of granular local level data and cutting-edge statistical methods