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duties, up to a maximum of 20% of full-time. Your research focus will be on theoretical models and simulations of thin film depositions, with a special focus on models for chemical reactions in combination
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relevant to both the healthcare sector and society at large. We are looking for a PhD student in Biomedical Engineering Sciences in the field of biological systems modeling and deep learning Your work
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, electromagnetic engineering, wireless engineering, engineering physics, applied physics, a closely related field. Good command of electromagnetic simulation tools such as CST Microwave Studio, HFSS or EM Pro
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applications towards materials science. Generative machine learning models have emerged as a prominent approach to AI, with impressive performance in many application domains, including materials discovery
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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
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multiagent dynamics, with special focus on human decisions and opinion dynamics. The research will deal with both theoretical and computational aspects. The student will develop dynamical models and apply them
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Professor in Theoretical Computer Science at LiU. The research for the advertised position will be within the WASP PhD project ”Model-Based Attention for Scalable AI Planning ”, where we will integrate
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Health Research and Policy-Work on Post-Covid-19 Syndrome ’ . Specifically, you will be working in the subproject ‘A Novel Model for Policy-Work’. One of the aims of this subproject is to examine what is
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modeling of magnetic materials using first-principles methods. Good knowledge of programming is required. Meritorious experience for the position is demonstrated knowledge of Git, Python, Bash and VASP. Good
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security analysis by providing the means and the tools to leverage model structures (e.g., dynamic key dependencies) in models of security mechanisms and to use these structures to automate security analysis