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. statistics, mathematics, computer science, statistical or population genetics, or a related discipline), and a strong motivation to work on problems in genetics and you will also have relevant coding
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design, interpret results and perform model validation. Contribute to reproducible modelling workflows (version control, documentation, shareable code and outputs) and participate in production of open
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across the study team including: overseeing data transfer agreements between institutions; version-controlling and archiving code and leading data cleaning, harmonisation and analysis. Throughout
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relevant PhD or equivalent qualification/experience in a related field of study, and will have experience in the coding and development of RShiny apps. The successful applicant will be a nationally
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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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: Comprehensive personalised Assessment, early Risk Evaluation and clinical management” (HER-CARE) project. The successful applicant will work on the project “Assessing the role of rare germline non-coding genetic
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sequencing etc.) Expertise in at least one lineage of land plants (i.e., a taxonomic focus) Experience with at least one coding language or a strong background in statistics Experience with high-performance
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neuroscience or developmental biology or equivalent Excellent skills in mRNA techniques, especially HCR labelling Confocal microscopy skills Computational experience, including coding Experience in using
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discipline *Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant. Expertise in atmospheric physics and chemistry Enthusiasm for coding of complex atmospheric
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learning in chemistry would be advantageous, as would familiarity with ML approaches for atomistic modelling (e.g., MACE, ACE, NequIP, PhysNet, reactive MD). Prior contributions to scientific code