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August 2025 Apply now Machine Learning models are increasingly important in the atmospheric sciences. After training, they can emulate model outcomes at a fraction of the computational cost of traditional
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and data acquisition. Collaborate with EXIT071 to advance dzITP technology for diagnostic and drug discovery applications. Optionally mentor or collaborate with MSc and PhD students involved in
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are offering a senior postdoctoral position within the Stranding Research Programme external link , part of the Division of Pathology at the Faculty of Veterinary Medicine. Your job The Stranding Research
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. Qualifications We are looking for a candidate with A PhD in Computer Science, Operations Research, Applied Mathematics, Mathematics, Engineering, or a related discipline. Strong programming experience, for example
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; developing visualisation prototypes to communicate uncertainty to end-users; contributing to a computational framework for data production in cooperation with Research Software Engineers; working closely with
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to involve the postdoctoral researcher in the supervision of a PhD-candidate, and we encourage and facilitate publishing and attracting research funding beyond the involvement in PERSIST. In short, we try to
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learning and data augmentation for soil and biomass carbon forecasts; developing a computational framework for data production in cooperation with Research Software Engineers; collaborating and coordinating
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, including grades. Your PhD thesis. Starting date of the position: flexible, currently aimed to start around February 2026. For more information regarding this position, you are welcome to contact Dr. Silke
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic
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: A PhD in Computer Science, Engineering, Mathematics, theoretical Physics or other degree programs from top universities involving at least one of the following topics: Machine Learning, AI, Dynamic