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Materials Science are found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production - and in the interaction between these areas
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developing AI methods for automated microstructure analysis and 3D microstructure generation. By combining self-supervised learning and diffusion-based generative models, the goal is to: Reconstruct high
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thorough understanding of theoretical/numerical methods for simulating optical phenomena, experience in fabrication and/or characterization of micro- or nanostructures, hands-on experience with fiber-optic
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on the following criteria: Knowledge in electric power engineering, power electronics, and power system analysis Experience in modelling, simulation, and experimental work Proficiency in Swedish and English, both
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, and will apply deep learning to integrate the analysis flows. The PhD student will develop the method and apply to numerous in-house samples of environmental sequences, pushing the boundaries of RNA
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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 Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation Product Development Material Production and in the interaction between these areas. The research covers
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, flexible and adaptable distributed system of systems. Example of specific problems are: -Information interoperability supported by ontologies. -Unified data models for operational environmental impact -SOA
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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