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are looking for a person with a relevant degree (e.g. health economics, economics, statistics, data science, public health science). Are you passionate about contributing to a groundbreaking interdisciplinary
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experience in the following fields. Cyber-physical modelling and simulation Digital Twins Autonomous Agents and Multi-Agent Systems Machine Learning and MLOps Probability & Statistics incl. Python/R Place of
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that rely upon interdisciplinary competences in: communication theory, networking, information theory, physics, mathematics, computer science, and statistics. This PhD project falls under Research Thrust RT3
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model checking. You should be well versed in basic statistics and practical programming skills is a must. Knowledge about the inner workings of GenAI would be nice but not necessary. You must have a two
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., linear algebra, statistics, optimization, and calculus) is expected, along with programming experience using deep learning frameworks in Python (e.g., PyTorch). While prior knowledge of machine learning
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statistics. This position will be placed under Research Thrust 2 which mainly involves mathematical physics. Requirements: The applicants must have documented strong qualifications in functional and spectral
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. This entails new models for integrating choice and process data, new statistical inference procedures tailored to such models, and new methods for collecting rich behavioural data in immersive experiments
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is to create and combine knowledge on relevant atmospheric flow statistics with AWE time-domain analysis and uncertainty quantification, to determine loads statistics and failure probabilities
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to investigate the mechanisms and cell-type specific effects of genetic variants. We are seeking a PhD student to join this effort, focusing on developing computational and statistical methods to model enhancer
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DTU Tenure Track Researcher in Nutrition, Sustainability and Health Promotion with a focus on Sus...
xperience with quantitative research methods, Experience in reproducible statistical analyses (preferably R and Python) and in the peer-reviewed publication of results. Must be able to work interdisciplinary