11 modelling-and-simulation-of-combustion-postdoc PhD positions at Linköping University
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, postdocs, researchers, and industry. Your qualifications You have graduated at Master’s level or completed courses with a minimum of 240 credits, at least 60 of which must be in advanced courses in
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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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, 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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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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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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in humans and in animal models. Environmental factors have been reported to predict the risks of developing SUDs too. For instance, epidemiological data have shown that impoverished social environments
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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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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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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