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Bioinformatics to join the Cancer Genetics team on the Prostate Cancer UK funded project “Identifying patients with aggressive prostate cancer by detecting molecular subtypes in urine”. This project aims
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trafficking and translational regulation. Our bioinformatics analysis reveals that these localised transcripts have a high statistical association with a number of important neurodegenerative diseases
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bioinformatics. Emerging cross-disciplinary areas of development include immunoengineering, gene therapy, neurotechnology, biomedical additive manufacturing and pediatric bioengineering. There is a strong support
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bioinformatics or programming (R/Python) is highly desirable. We welcome applicants from diverse backgrounds who are enthusiastic about combining molecular (wet-lab) and computational approaches to uncover how
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student with a degree in plant biology or microbiology and a keen interest in chemical soil ecology. Experience with bioinformatics or programming (e.g. R/Python) is desirable. We welcome applicants from
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, bioinformatics; Utrecht & Sheffield) High-resolution metabolomic analyses (LC-MS/MS, MALDI-timsTOF imaging; Sheffield) Computational integrative analysis of plant transcriptomic, metabolomic, and microbial
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This project seeks to study the diversity and evolutionary history of giant Starship transposons within fungal genomes. In this project the RA will be directly involved in the bioinformatic identification
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Infrastructure, and High-Performance Computing; Visual Computing, Medical Informatics, and Bioinformatics; Web Intelligence and Recommender Systems; Computing Education; Interactive Machine Learning; Cybersecurity
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Bioinformatics laboratory course, and additional courses in the Biology undergraduate curriculum. This is an academic year, benefited, 2-year position and is renewable contingent upon performance. Lecturers
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team members on computational and experimental efforts. The role requires a background in neuroscience, molecular biology, bioinformatics, or a related field, with experience working with large datasets