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networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid academic background with thorough computational and
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aims to build predictive and physical binding models of protein – DNA interactions using high-throughput and quantitative biochemical binding data across hundreds of thousands of sequence variants
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and motivated PhD student to join an interdisciplinary project that combines computational biology, spatial transcriptomics, and tumor modeling to understand how the aggressive brain tumor glioblastoma
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in Python programming. Experience with machine learning methods, bioinformatics, and data science. Familiarity with generative AI tools for protein design and protein language models. Knowledge
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components in time and space, from single molecules to native tissue environments. The project The industrial PhD student will develop AI and machine learning models to predict drug metabolism, a critical area
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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to
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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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Associate Professor Åsa Johansson at Uppsala University, Department of Immunology, Genetics and Pathology. The group focuses on identifying risk factors for common diseases and developing models for risk
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reconstitution to study the replication of positive-sense RNA viruses. This is a vast group of viruses responsible for serious diseases such as Dengue, Chikungunya, TBE and viral myocarditis. To copy their genomes
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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