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will use machine learning methods to develop affinity ligands. These methods have been transformative for protein design, allowing generation of novel proteins which can suit a precise need. In this 4
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and documented background in machine learning, deep learning, data analysis and programming. Previous experience in research and knowledge in bioinformatics, biophysics, biochemistry, molecular biology
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and/or functional imaging or application of computational modeling, machine learning and AI to understand cellular function. At least five years’ experience working within the university system, another
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managing large amounts of data by designing structured databases (PostgreSQL, MySQL). Machine learning methods such deep learning for analysis of proteomics data and classification of cancer profiles. Since
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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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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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than 40 PhD students and postdocs. Research at the DSAI ranges from foundational methods in machine learning (e.g., optimization, bandits and reinforcement learning) to application domains in biophysics
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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for improved cancer understanding, diagnostics, and
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of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
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at the single-cell level, using tools from optimal transport, mathematical optimization, and machine learning. In addition to method development, the work includes applying and benchmarking algorithms on both