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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The PersonalRiskProfile project aims to develop advanced genetic prediction
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applying high-quality mathematics and statistics, while offering educational math and statistics programs in a friendly and intellectually stimulating environment. We are dedicated to expanding the frontiers
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, train, and evaluate reinforcement-learning agents that play classic and modern video games. Your work will focus on automated gameplay analysis, feature engineering from large-scale behavioural data, and
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develop teaching skills and explore whether you see a future in academia. Where to apply Website https://www.academictransfer.com/en/jobs/357311/phd-position-in-probabilistic-a… Requirements Specific
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interested in how floods are generated and which processes can lead to floods? Do you want to understand how extreme events manifest and how they differ in space and time? Do you want combine statistical and
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, mathematical logic or statistical learning theory. For PhD position 2, we appreciate prior experience in implementing deep learning models for graphs and networks. Our offer As a PhD candidate at UT, you will be
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acquiring new, challenging research projects. Where to apply Website https://www.academictransfer.com/en/jobs/357518/researcher-genetic-resources-of… Requirements Specific Requirements We are looking for a
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. By integrating the exposome into the risk-trigger-vulnerability model, we aim to achieve a more holistic understanding of CHD, including myocardial infarction (MI), and to develop more precise and
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external and internal factors contribute to IS risk. By integrating the exposome into the risk-trigger-vulnerability model, we aim to achieve a more holistic understanding of IS and to develop more precise
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of everyday life. This project aims to change that by developing AI-driven methods to assess wellbeing through video-based sentiment analyses. As a PhD student, you will develop and refine machine learning