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, Physics, Electrical Engineering, Communication Engineering, or equivalents; – Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation
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(preferably in Python, including deep learning frameworks such as PyTorch); affinity with medical image analysis and computational modeling; experience with neural networks for image analysis, generative
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learning environment offers a wide range of training courses, e-learnings, and educational programs—from professional specialization to personal development. This allows you to keep growing and make the most
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improved using machine learning techniques. The developed techniques will be applied to metrology of semiconductor samples. Job requirements You are an enthusiastic candidates with a ‘drive’ for applied
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actively on the preparation and defence of a PhD thesis in the field of explainable reinforcement learning (XRL). Explainable reinforcement learning aims to make decisions, policies, and learning processes
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actively on the preparation and defence of a PhD thesis in the field of continual reinforcement learning. Continual reinforcement learning studies how agents can learn across a sequence of changing tasks
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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
for the position. Preferred selection criteria Experience with machine learning or other relevant AI technologies Scandinavian language skills Previous experience from industry or research in engineer-to-order
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assurance for embedded systems by combining advanced side-channel analysis, fault-injection techniques, AI- and machine-learning-assisted analysis, robustness evaluation, and quantitative security assessment
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to the faculty's Doctoral Programm e, see Section 6-1 of the PhD regulations for more information. You must have a relevant Master’s degree in computer science or equivalent. Your degree must include a major
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have affinity with data collection tools? Within the project ReGeNL, we are looking for a junior researcher to support the monitoring and learning activities within ReGENL. There is steadily growing