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Field
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to: Development of computer models of the musculoskeletal system incorporating neural control pathways Train NMS model control policies via RL Your seconday tasks will include: Using learned NMS model control
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We’re seeking for motivated candidates that are interested in developing computer models of the composite human neuro-muscular system that combine detailed musculoskeletal geometries, muscle-tendon
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boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system diagnostics and
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. This simple unit is limiting the learning capabilities of recurrent neural network models in tasks characterized by multi-timescale and long-range temporal dependencies. To implement multi-scale adaptation, in
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Vacancies PhD Opening: Reinforcement learning in human neuromusculoskeletal models for the control of human-inspired musculoskeletal robots. Key takeaways We’re seeking for motivated candidates
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enhance real-time decision-making in road traffic management. The project aims to bridge the gap between recent advances in AI and machine learning, in particular, multimodal and instruction-tuned
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in Computer Science, Artificial Intelligence, or related field. Solid programming and development skills (Python, Git, Bash). Experience with machine learning (e.g PyTorch/TensorFlow). Strong interest
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-antibodies. You will focus on the identification of these antibodies by using mass spectrometry based de novo sequencing, machine learning and AI-tools to interpret the data. Your job The primary objective
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mass spectrometry based de novo sequencing, machine learning and AI-tools to interpret the data. Your job The primary objective of the project is to further develop mass spectrometry-based techniques
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning