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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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-target interactions, and optimize them towards validation as preclinical platforms alternative to animal model for drugs screening and development. The project is part of a collaborative effort and will
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achieve targeted modification of semiconductor properties through electronic doping to control and modify their electronic characteristics. The project’s goal is to develop fundamental understanding and
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of FADOS is to achieve targeted modification of semiconductor properties through electronic doping to control and modify their electronic characteristics. The project’s goal is to develop fundamental
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issues in federated and decentralized learning systems. The aim is to develop novel methods for securing communication against passive and active adversaries, leveraging tools from statistical estimation
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research on the development of new inference methods and algorithms for wide classes of stochastic models. However, research will be conducted in collaboration with biologically oriented researches allowing
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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and research. By developing systematic process descriptions, open manuals, and pedagogical models, knowledge transfer in real time will be made possible, ensuring that results can quickly be applied in
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to develop a theoretical foundation for understanding and mitigating memory poisoning in LLM agents. This position, funded by the Swedish Research Council (VR), offers an exciting opportunity to work
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at the intersection of AI and advanced electron microscopy. The project focuses on developing novel self-supervised and physics-informed deep learning methods to restore and denoise Transmission