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Field
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and have synergiccollaborationeffects. Weexpect a motivatedearlycareer researcher with stronginterest and experience with GIS/earth observation/climateprojection data as well as machine learning models
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fellows will receive joint mentorship from leading experts in metabolic biology, AI and machine learning, drug delivery, and translational medicine, while maintaining full academic independence in research
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, large language models, or computer vision is preferred Knowledge, Skills and Abilities: · Teamwork: Ability to collaborate with others and contribute to a team environment · Communication
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or datasets are also welcomed. Join an ambitious ERC-funded project and help advance predictive modelling at the interface of supramolecular chemistry, catalysis, and machine learning. The closing date
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to machine learning and AI projects for satellite systems. We are looking for a candidate capable of developing ML models and optimization algorithms specifically designed for highly dynamic satellite
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Germany | 22 days ago
AI in biology. The successful candidate will design and implement physics-informed machine learning frameworks and predictive models to uncover how gene expression and mechanical forces interact
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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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landscape constrains or enables discovery. The project draws on tools from topological data analysis (e.g., persistent homology, Euler characteristic curves, discrete curvature), machine learning (e.g
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for design consideration. Irradiated mechanical property prediction models and property correlation metamodels will be developed considering traditional and machine learning approaches. Extrapolation will be
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes