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’. The department undertakes fundamental research on model organisms, agricultural crops, forest trees and bioenergy crops. Our main areas of research comprise the interaction of plants with microorganisms and other
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theoretical research in nuclear physics, nanometer physics, quantum information, atomic physics, and modeling of materials, with several collaborations internationally and within Lund University. Subject
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of the project The PhD student will study how immune cells affect atherosclerosis development through studies of animal models and human tissue. Through translational projects, the PhD student will investigate how
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competence, and a results-oriented and proactive attitude. Meritorious for the position are: Previous research related to CMDs, longitudinal data modelling, human genetics. Assessment Criteria and Selection In
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modelling tools (e.g., CLEWIN, ANSYS, COMSOL) -Strong analytical and problem-solving skills, with the ability to work independently and within interdisciplinary teams -Fluency in both spoken and written
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understanding how mitochondria function within the metabolic system. For this, we use model systems, ranging from patient-derived samples, reprogrammed neuroepithelial stems cells to genetically modified animal
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model systems, ranging from patient-derived samples, reprogrammed neuroepithelial stems cells to genetically modified animal models. We are specifically recruiting for a project studying nuclear genes
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will combine state-of-the-art computer vision, modeling and archived specimens to determine biotic and abiotic factors driving spatial variation in molt phenology. It will use museum genomics to recover
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research and methodological development to design and implement novel computational models and solutions. A solid theoretical background and hands-on experience in digital image processing and deep learning
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., text reasoning, language models). -Knowledge of generative AI models (e.g., transformers, GANs) for text, image, or cross-modal data. -Familiarity with self-supervised learning and weakly-supervised