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to the 30th September 2026. We are looking for outstanding machine learning researcher to join the Torr Vision Group and work on AI Scientists: systems that use foundation models, AI agents, and robotics
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dependence on host physiology. The position is part of the project “Universal Growth Laws of Viruses of Bacteria”, led by Associate Professor Namiko Mitarai and funded by the Novo Nordisk Foundation NERD
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
vulnerabilities. The Postdoctoral Appointee will be responsible for the conceptual framework, design, and implementation of these models, ensuring scalability on the DOE’s leadership computing facilities. Position
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. Essential Criteria PhD qualified in relevant subject area (or pending results/near completion) Experience applying multi-modal models specifically in medical or clinical domains. Strong knowledge of MRI data
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others, pseudospectra theory, and empirical data from DRAGNet and global demographic databases (COMPADRE, COMADRE, PADRINO). The project combines mathematical modelling, simulation, and empirical synthesis
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of the model or their parameterization based on the results from the model-data comparisons Publish results in scientific journals with highest scientific standards Your Profile PhD degree in Ecology
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The project is a follow-on to a project conducted at CAMS on modeling the spread
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typically apply these models to understanding brain development and diversity. So an interest in, or experience with, studying development would be highly beneficial. You should hold a PhD in a relevant field
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related to staff position within a Research Infrastructure? No Offer Description Call for Postdoctoral Researchers in Artificial Intelligence (AI) – Focus on Large Language Models (LLMs) for Predictive
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automatically distinguishing between relevant and irrelevant data, developing appropriate model evaluation criteria for these models, solving a realistic, high-dimensional asset allocation problem with