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Field
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of innovative computational procedures and methodologies, addressing a national skills shortage and enabling timely progress on a high-impact research initiative in modern econometric modelling. The role provides
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: Develop novel machine learning theories and techniques for analyzing noisy time-series data, with a particular focus on seismic signals Perform uncertainty quantification in time-series analysis to assess
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antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and
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AI/machine learning, and data analysis using MATLAB or Python. Provide technical assistance on related research projects, such as preparing progress presentations and reports for funding agencies
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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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data, spatial modelling, multivariate statistics and/or machine learning, and relevant coding languages (e.g. R, Python), including a sound understanding of FAIR data principles, data management and
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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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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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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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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