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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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to build sequence dependent predictive deep learning models, and physical mechanistic models (thermodynamic and kinetic models etc.). Examples of suitable backgrounds: machine learning, programming
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Sciences division. This multidisciplinary team utilises a combination of machine learning and mechanistic modelling to derive models and scientific insights from data, which both support and enhance drug
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evolutionary analysis. A central component of the research will be to develop machine learning and deep learning methods trained on coding sequences and protein structure to extract patterns in data and to draw
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systems, and machine learning. While the initial focus of the position is on this project, we offer significant opportunity for the applicant to develop their own independent research trajectory in
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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and computational modeling to understand complex biological processes. Experience in statistical modeling, machine learning, or analysis of spatial or high-dimensional biological data is considered
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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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sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
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. For more information, see www.iob.uu.se . The Physiology and Environmental Toxicology program is a growing, vibrant research environment where experimental models and molecular tools are used to study and