88 assistant-professor-and-human-centric-computing Postdoctoral research jobs in Sweden
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The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
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techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get unprecedented medical
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tomorrow. About the department The main competences at the Department of Industrial and Materials Science are found in the areas of: Human-Technology Interaction Form and Function Modeling and Simulation
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carried out in the laboratory of Associate Professor Sara Wilson located at the Department of Medical and Translational Biology (MBT), Umeå University, Sweden (see lab web page ). MBT is an
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and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
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Do you want to help shape the future of sustainable materials? We are looking for a dedicated postdoctoral researcher to develop bio-adhesives and additives that meet both sustainability goals and
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sea food web dynamics over time in response to environmental factors and human-induced pressures. Within the project it is possible to work on all these parts or focus more on some of the questions
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of the organization. Contact Main Mentor: Associate Professor Reza Hosseinpourpia, 0470-70 80 74, reza.hosseinpourpia@lnu.se Head of the department: Erika Olofsson, 0470-70 89 99, erika.olofsson@lnu.se HR partner
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of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope that you will help us to
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description