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analog electronic accelerators. You’ll collaborate closely with a multidisciplinary team of machine learning experts, software developers, computer scientists, fabrication specialists, and experimentalists
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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‑on experience with common machine learning / deep learning frameworks (eg. PyTorch or JAX) applied to biological or structural data. Solid Python programming skills, with experience building maintainable and
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and mapping, light fields, extended reality (XR) technologies, sim-to-real, synthetic data generation, and advanced computer vision and machine learning techniques. In addition, the group works on
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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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. Position You will work actively on the preparation and defence of a PhD thesis focusing on machine learning-based forecasting of renewable energy production, with a particular focus on wind energy. The
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the discipline of bioinformatics, data analysis of large-scale (bio)medical data, applications of artificial intelligence and machine learning. You contribute to high-quality teaching in bachelor and master years
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) recordings, based on machine learning and signal processing methods. The work involves machine learning for image processing, processing and decoding of EEG signals, and EEG data collection. - You should
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and metrics may also support the development and evaluation of computer vision models for automated performance assessment and feedback. Ultimately, this research will contribute to the creation
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, technical depth, and a strong track record of applied research in Computational Biology, Structural Biology, Protein Engineering, Machine Learning, or a closely related field. Strong understanding and