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/ computer vision and pattern recognition, including but not limited to biomedical applications Strong interest in applied machine learning, including but not limited to deep learning Experience utilising GPU
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of material behavior to the development of the material to the finished component. PhD position on physics-based machine learning modeling for materials and process design Reference code: 980 - 2026/WD 1
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(graduated or close to graduation) in Computer Science, Computer Engineering, Artificial Intelligence, Machine Learning, Applied Mathematics, or related fields. Scientific curiosity and creative thinking
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, including machine learning and language technologies, for the integration and analysis of clinical, advanced data harmonisation, and next generation research infrastructures. You will contribute to research
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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-Holstein (UKSH), is seeking a PhD student with a strong background in statistics, machine learning (ML)/artificial intelligence (AI), or bioinformatics. Our group develops novel computational and statistical
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the area of enzyme engineering to the next level, while having a positive impact on our world. When joining our group, you get the opportunity to use the latest algorithms in machine learning for improving
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infertility, pregnancy, lactation and developmental programming, urogynecology, artificial intelligence and machine learning are particularly encouraged to apply. The Department of Obstetrics and Gynecology
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Research Assistant (m/f/d) with a Ph.D. in Civil Engineering, Engineering Physics, Physics, Mathemat
., using FEniCSx) Advanced knowledge of scientific programming, preferably in Python, including experience with implementing machine‑learning methods (e.g., PyTorch) Excellent spoken and written English, as
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from chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas