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membrane proteins involved in numerous physiological processes. By leveraging machine-learning enhanced virtual screening, the PhD student will be able to perform searches for ligands in chemical libraries
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Uppsala University, Department of Information Technology Are you interested in working in the area of scientific machine learning, with the support of competent and friendly colleagues in
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opportunity as an Industrial PhD student to work on a project that applies machine learning to improve diagnostic and reporting workflow processes in clinical kidney pathology. This project is a collaboration
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study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required. One Ph.D. position is available in cancer precision medicine using advanced machine learning
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trials, cutting edge genomics, machine learning and artificial intelligence. The doctoral student project and the duties of the doctoral student At the Department of Medical Epidemiology and Biostatistics
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analysis based on machine learning and deep learning/AI. You will work with shorter and longer projects and offer research support via the BIIF nodes in Uppsala and Stockholm, and other universities in
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primarily work with the analysis of large-scale data from DNA methylation arrays and SNP arrays, along with prospective clinical data. You will perform multiome analyzes and use machine learning methods
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learning methodologies to extract conformational ensembles from single-particle cryo-EM data. The project builds on our recently established (not yet published) software, which machine-learns protein
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. This research project is a collaboration between Eric Libby and Laura Carroll. It involves using machine learning techniques to infer the mechanisms by which microbes make decisions, bioinformatic techniques
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neurodevelopmental reconstruction using a machine learning approach and beyond. Combo of single cell and spatial transcriptomics adds a critical layer of information by mapping gene expression patterns to specific