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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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. 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
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at Uppsala University can be found here . Project description Digital pathology and detection of cancer based on hematoxylin and eosin (H&E) stained tissue samples has made enormous progress in
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rate, and virtually nothing is known about a putative connection between these mutation rates. Using several Drosophila melanogaster model systems, in combination with quantitative genetics, experimental
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methods, including modern machine learning methods, to draw inferences from register data. A third project “Integrative machine and deep learning models for predictive analysis in complex disease areas“ is
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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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evaluation frameworks and/or the development of energy system optimization models. The research is applied and closely linked to industrial interests and needs. About the research Our research aims to provide
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) classification and utilization based on advanced AI technologies, such as regenerative AI, image processing and reinforcement learning, that can improve the energy efficiency and reduce the operating cost and
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immunity and develop diagnostic approaches that accurately predict therapy benefit and enable successful individualized cancer therapy planning. Contemporary AI-based approaches show great promise to advance
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qualifications Marine biogeochemical processes Hydrodynamic processes related to ships, turbulence, or mixing Oceanographic modelling Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary