594 parallel-processing-bioinformatics-"DIFFER" Fellowship positions in United Kingdom
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skills and knowledge of computer programming such as Unix, Python and R Previous experience with multi-omics and their integration Deep technical understanding of bioinformatics analysis approaches and
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the Faculty of Life Sciences & Medicine Hub for Applied Bioinformatics. This post is jointly funded by the Borne Foundation (50%) and King’s Health Partner’s Centre for Translational Medicine (CTM) (50
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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and
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(where possible) in BioModels and other repositories Generation of synthetic data to represent rare disease patients that can be shared You have A PhD in bioinformatics, physics, or a related data
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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and
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Surgery, in the Department of Clinical Science and Services. You will be expected to enhance the department’s reputation through scholarship in clinical activities and teaching. You will achieve this by
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. Remote working may be feasible depending on the nature of work tasks being performed. You will be working closely with Professor Regent Lee’s team. You will be carrying out research for different research
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to contribute to an exciting European project developing hydrothermal processes for the management of solid digestate and other waste from the olive oil production process. About the Role We are seeking a
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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the Medical Research Council. The Research Fellow will be using Natural Language Processing (NLP) methods, with a special focus on generative Large Language Models (LLMs), to interrogate a very large sample of