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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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large, multimodal human datasets. Emphasis is placed on the candidate’s personal qualities, including independence, strong organizational, communication and collaboration skills, social and professional
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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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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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-assembly on a structural level, and correlate this with in vitro functional activity. At AstraZeneca, the student will be integrated into the Data Science and Modelling department within the Pharmaceutical
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vision, machine learning, deep learning and neural networks, as well as courses in python, GPU programming, mathematical modeling and statistics, or equivalent. We are looking for candidates with: A solid
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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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pharmacy or have similar experience. Great emphasis will be placed on personal qualities such as good collaborative skills, motivation and independence, as well as how the applicant through his/her
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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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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