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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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, computational modelling, bioinformatic analysis, and experimental vascular biology. Based in a dynamic translational research environment of data-driven life science, computational imaging, and vascular surgery
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: Master’s degree in biomedicine or biostatistics. Doctor of medicine degree with clinical practice experience. Certified training in R and Python software. Documented experience using machine learning and
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methods in applied mathematics and computational modeling, this specific project aims to uncover new insights into how blood cells form in both healthy and disease states. A key objective is to model
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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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of researchers (senior scientists, post-doctoral fellows, graduate students and research engineers) with different areas of expertise. The research program is multidisciplinary with competence in computational
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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