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, conservation genomics, museomics, metagenomics, annotation, machine learning, Instruct users in the usage of hardware and software for molecular biodiversity research, Acquire substantial third-party funding
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epidemiology, simulation modeling, machine learning, and health economics to 1) improve health outcomes for vulnerable populations, 2) uncover structural factors that drive health inequities, and 3) inform
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microbes chemically/physiologically. Computational work could include genomic analyses, machine learning, and simple mathematical modeling approaches. Responsibilities Responsibilities include the following
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at industry-facing events. Strong technical and scientific knowledge in machine learning, preferably with experience in large language models (LLMs). Solid foundations in mathematics and engineering
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using state-of-the-art computational approaches such as machine learning, process-based, or statistical models. The position includes advising graduate students in the Ecology, Evolution and Organismal
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a role model and fostering an inclusive working culture. Person Specification PhD, or close to completion, in a relevant, quantitative field, e.g. meteorology, machine learning, climate science
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written and spoken English skills High degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very
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. when do we stop modelling? How do we track / score the quality of the model? What is the required level of quality over time? How can quality be brought to the required level? Can Machine Learning, Large
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, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded methodology project
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machine learning and computer vision algorithms • Build tools for photogrammetric 3D reconstruction and defect detection • Evaluate and refine AI models using annotated data and real-world image datasets