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environmental data Processing and analyzing large-scale remote sensing datasets from UAV, satellite, and ground-based sensors Leveraging artificial intelligence, e.g. machine learning, reinforcement learning
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in statistics, 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
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decision tool based on SLP/NLP, and utilizes large language models. The project will focus on interaction with clinicians, with a goal of closing the gap between foundational research in machine learning and
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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning
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computational materials science techniques (DFT, MD, machine learning force field modelling) with data-driven approaches. Work with team to design and implement high-throughput experimental workflows for rapid
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from date of hire. Preferred Qualifications Aptitude and experience with: (a) predictive machine and deep learning techniques, (b) statistical analysis, (c) hands-on experience using models such as
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computer programming to verify the efficiency of the designed solution algorithms Analyze data acquired from the field survey Develop machine learning models for prediction and recommendation Job
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position under the supervision of Dr. Chris Smith Home | Chris Smith . Our new lab in Evolution, Ecology, and Behavior investigates machine learning approaches for population genetics. Our research
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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, policy, energy conversion, new business models, techno-economic and life cycle analyses, machine learning, optimization, AI, intelligent networks, among others. The PDF will join a project in collaboration