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Aluminium through Machine Learning, High-Throughput Microanalysis, and Computational Mechanics” - a multidisciplinary research effort at the intersection of machine learning and materials science. This
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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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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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. 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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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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for Quantum Technology (WACQT, http://wacqt.se ). The core project of the centre is to build a quantum computer based on superconducting circuits. You will be part of the Quantum Computing group in the Quantum
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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
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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