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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
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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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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
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for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm development. Basic familiarity with experimental