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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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. These include, but not limited to: Research Question 1: How can multimodal UAV data (RGB, thermal, LiDAR, hyperspectral) be fused using machine learning to predict complex canopy traits such as water-use
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The Department of Agroecology at Aarhus University, Denmark, is offering a postdoctoral position in machine learning for advanced peatland mapping, starting 01-12-2025 or as soon as possible
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, you must hold a PhD degree (or equivalent). The successful candidate must moreover exhibit the following professional and personal qualifications: Strong background within machine learning learning, and
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. theses at the interface between structural engineering and machine learning. You will disseminate your research through peer-review publications and participation in international conferences. You will
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that are commonly applied to learning and teaching practice in computer science education. Employment Terms The PhD student is expected to teach FNUG’s courses MM107 Dynamic Systems and Interdisciplinary Subject
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. The consortium consists of world-class scientists with competences spanning chemistry, biochemistry, computer science, and machine learning. All fifteen doctoral candidates will work with two research groups, and
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Engineering. Therefore, the department invites applications from candidates who are driven by excellence in research and teaching as well as external collaboration on societal challenges. The position will be
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. Measurement techniques in field applications and in the laboratory. Modeling and simulation skills (batteries, energy systems, electric equivalent circuits). Machine learning, statistical analysis, and other
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health data, such as electronic health records or biobank-scale resources (e.g., UK Biobank, All-of-Us, FinnGen). Familiarity with machine learning approaches, such as penalised regression, deep learning