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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental
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mindset and intellectual curiosity to strengthen and complement the research profile of the Mathematical Insights into Algorithms for Optimization (MIAO) group at the Department of Computer Science at Lund
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chemical vapor deposition, its characterization and optimization. Design sensor layout and evaluate materials involved, from the standpoint of bio compatibility. Functionalize graphene devices, in
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sensors and sensor fusion of multiple event-cameras in a real-time human-robot co-working space, where a robotic manipulator (a 7 DOF robotic arm) interacts with a human subject and multiple hand-held tools
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covers theory and algorithms for goal-oriented, semantics-aware communication that enable efficient, intelligent, and adaptive information exchange in autonomous systems. The particular focus
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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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and technical skills within a team of leading researchers in structural and algorithmic graph theory. The project focuses on exploring the tractability and intractability of graph problems in both
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algorithms and methods for calibrated Bayesian federated learning for trustworthy collaborative Bayesian learning on data from multiple participants. The project will develop new methods, theory, and
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, machine learning or similar. Alternatively, you have gained essentially corresponding knowledge in another way. The applicant is expected to have good knowledge of computer science, mathematics, algorithms
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, 6G communication and wireless sensor networks as well as research and education within life science, health technology, smart electronic sensors and medical systems. The Department of Electrical