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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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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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. Rocío Mercado Oropeza, applies machine learning to molecular engineering problems in life sciences and drug discovery, and is based in the Division for Data Science and AI within the CSE Department
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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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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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Uppsala University, Department of Information Technology Are you interested in developing new image analysis and machine learning methods for precision medicine and clinical decision support? Would
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variability in risk factor susceptibility, treatment response, disease pathogenesis, and clinical diagnosis (biostatistics, machine/deep learning), ii) Investigating causal processes and disease mechanisms
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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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in computer science, engineering, data sciences, applied mathematics, machine learning, or another related field; or Have completed at least 240 credits in higher education, with at least 60 credits at