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We are offering a PhD student position in machine learning (ML) theory, focusing on new methods for training models with a limited amount of data. The student will be a part of a new NEST initiative
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application! We are looking for a PhD student in theoretical physics with a focus on the theory of magnetic materials. Your work assignments Your tasks will be to carry out research using advanced theoretical
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application! We are looking for one or two PhD students in Multiagent Automatic Control at the Information Coding Division (ICG), which is based at the Department of Electrical Engineering (ISY). You will be
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structures, etc to solve challenging problems is required (there will be a practical coding assessment during recruitment) A solid mathematical foundation is required (multivariable calculus, linear algebra
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the intersection of control theory, robotics, artificial intelligence, and signal processing, and is a key enabler of future intelligent infrastructure.This project will focus on the development of novel control
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at compile-time, as is often the case, then the problem is further complicated by the fact that no single mapping is optimal for all combinations of matrix sizes. As a consequence, any code generated
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Engineering and Autonomous Systems division . We offer advanced PhD courses where we extend the fundamentals in optimal control, machine learning, probability theory and similar. The research and learning
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application! This position is in a lab with an interest in many aspects of theoretical ecology (coexistence, niche theory, ecological networks, metacommunities, eco-evolutionary dynamics, and more), where you
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covers a range of topics—from microbial ecology theory to process engineering. We develop new treatment processes and investigate the fundamental science behind them. Examples of our research areas include
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research spans microbial ecology theory to process engineering, and includes the development of new biotechnological processes. Examples of previous research areas include: Anammox Fermentation Microbial