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considered. The research will involve data collection, model building and analysis of potential benefits of improved logistics practices and transition to low-emission vehicles. The successful candidate will
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based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies
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. You enjoy combining experimental laboratory work with theoretical analysis and modelling. While your main focus will be the research project and your own development as a researcher, the position also
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an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. The research group The position is based in Joakim Dahlin’s team at Karolinska Institutet
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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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responsibilities will include investigating the structural and biophysical properties of miniaturized tumor environment models. You will be responsible for fabricating these systems using both cleanroom-based and
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format. This will allow combinations of neural networks with physics models. The project brings together PhD students and senior researchers from multiple disciplines to tackle challenges in sustainable
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staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition. Lund University welcomes applicants with diverse backgrounds
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be paid to the following experiences: -Experience in sampling and analyses of building materials -Experience in Life Cycle Analysis in construction sector. -Experience in building information modeling
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. The long-term goal is to enable targeted interventions for the right individuals, based on their lifestyle, disease trajectories, and molecular profiles. To achieve this, we will apply deep learning models