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Field
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reinforcement learning and other approaches for cross-domain generalization Qualifications Essential: PhD in Computer Science, Machine Learning, Computer Vision, Natural Language Processing, or closely related
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experiments (both in the lab and in the field), coordinating cross-cultural studies involving multiple international research groups, data analysis, computational modeling, and writing scientific articles
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, analytical modelling, and numerical simulations to develop and validate novel reinforcement systems. This position is part of an Innosuisse-funded collaborative project with industry partners, focusing
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) detection, we will make AI for medical imaging transparent, robust and data-efficient. Position Overview As a postdoctoral researcher, you will forge next-generation explainable medical foundation models. You
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. Responsibilities: Conduct research in compilation, optimization, and analysis for time-predictable computer architecture. Co-supervise MSc and PhD students. Contribute to teaching and research proposal preparation
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Postdoctoral Research Associate in Global Environment Modelling of Soil Organic and Inorganic Carbon
. The project is aimed to improve our in-house developed process-based computer model and use it to represent the soil ecohydrological and biogeochemical interactions across various carbon and nitrogen soil pools
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. Applications for this position should include a: (1) Curriculum Vitae with the names and contact information of 3 references (2) Diversity Statement (3) Half-page summary of research experience and interests
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stimulationDURATION: 18 months (potential extension)LOCATION: Université de Montpellier – LMGC, in collaboration with LIRMM, IRMB, and the industrial partner SymétrieSTART DATE: End 2025---POSITION OVERVIEWThe funded
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experience on multiscale modeling, hybrid modeling, and model-based control or related fields. Additional Information: Location: CBEC (0248) Position Type: Term (Fixed Term) Scheduled Hours: 40 Shift: First
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Associate with mathematical modelling and numerical/data analysis background to join our food system resilience project, led by University of Reading, joining a large interdisciplinary team with an excellent