138 phd-in-mathematical-modelling-of-biochemical-reactions Postdoctoral positions in Sweden
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of relevance: Documented expertise and skills in mathematical modelling of community dynamics is a requirement, and proficiency in statistical time series analyses in community ecology is desirable. Skills in
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electronic structure calculations Experience from modeling surface reactions Documented experience in programming Excellent communication skills in written and spoken English Swedish is not a requirement but
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immunity through detailed molecular characterization of these disease models. Joanna Rorbach’s lab: Our current research encompasses genetic, structural, and biochemical studies to understand the regulation
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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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of new biochemical and analytical methods are the basis for advanced studies of biological systems and their regulation, and how diseases can be diagnosed and treated. The department benefits from close
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Parkinson. We use in vitro biophysical analysis to characterise protein aggregates and their formation in combination with advanced live cell fluorescence imaging and cell model development to study protein
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to illness, parental leave, clinical service, trade union duties or similar circumstances. The PhD degree should be in a computational or life science field, such as bioinformatics, ecology, mathematics
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crystallography, cryo-EM, molecular biology, as well as biochemical and biophysical approaches. A more detailed description of the project can be found at https://www.biostruct.umu.se/principal-investigators/karina
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considered a strong advantage: Biochemical methods for studying RNA–protein–DNA interactions. Use of animal models for in vivo studies. Histological techniques, including in situ hybridization and
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mathematics, ecology, history, climatic and medical sciences in collaboration across multiple institutes. An integral part of the project is to develop process-based eco-epidemiological models considering