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, provide an assessment of the application. Please indicate their relationship to you Early application is highly encouraged, as the applications will be processed upon reception. Please apply ONLINE formally
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Eligibility criteria Training in organic chemistry Experience in synthesis Proficiency in analytical methods for synthesis Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5278-ANITLI-009
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methods for synthesis Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR5278-ANITLI-008/Default.aspx Work Location(s) Number of offers available1Company/InstituteLaboratoire Hydrazines
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along with complementary structural biology methods. The successful fellow will work at the Institut Laue Langevin (ILL) to develop and apply cutting-edge neutron crystallography approaches
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of visitor numbers in natural environments, particularly in protected areas. Analyze and compare the methodological approaches identified (indicators, models, feedback). Formalize and further develop
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Explainable AI on genomics data The candidate will: - Start by familiarizing with existing research and methods for genome interpretation - Familiarize with the sequencing data and its pre-processing - Study
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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of statistics is preferred. Experience with experimental work and molecular ecology methods is an asset. The candidate must demonstrate proven ability in independent scientific research and skills in writing
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to propose a new method of producing a molecule from the corticosteroid family by adopting an innovative approach combining an original and sustainable synthesis route with a production method based on
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- Collaborate with theoretical physicists specialized in optimal control methods - Set up experiments and evaluate the performance of control protocols - Conduct metrological studies of an atom gradiometer using