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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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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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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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, 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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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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conditions The doctoral student will be employed on a doctoral studentship maximum 4 years full-time. Application process Submit your application and supporting documents through the Varbi recruitment system
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atmosphere. The mid-sized city (100,000 inhabitants) provides affordable housing, numerous cafés and restaurants, excellent sports and cultural facilities, and easy access to scenic nature reserves, hiking
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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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The research in Theme A provides opportunities to address issues on measuring, assessing, and modelling of Quality of User experience (QUX). This includes personalizing QUX in novel intelligent realities