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Field
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strong background in machine learning, computer vision, or data-driven modeling. You have extensive experience in the development and implementation of AI and machine learning algorithms, ideally with
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spatial and seasonal distributions of PFAS and identification of the key processes controlling their fate (dispersion, transformation, sediment retention). Contribution to modeling PFAS transport in
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an exciting approach to agentic, fully autonomous thin film development using a combination of automated electroplating, in-operando measurements, and AI driven algorithms. He or she will work with a team of
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the microclimate within an agroforestry system is essential for optimizing tree distribution, with the aim of maximizing yields while limiting inputs and mitigating the environmental dispersion of contaminants
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of Machine-Learning/ Statistical /Probabilistic models and algorithms. The hiring unit will not sponsor employment-eligible immigration statuses for this position. Environmental Studies Department: https
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the necessary data from numerical models and observations to build the dataset; Identify the algorithms best suited to learn the targeted behaviors; Train the learning models; Validate their ability to predict
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training algorithms and AI architecture. Image reconstruction, segmentation, and classification. High performance computing for spatiotemporal data. Major Duties/Responsibilities: Develop foundation AI
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Location: Mansfield Road, Oxford OX1 3QT We are seeking a creative and motivated neuroscientist to investigate how distributed brain circuits support flexible learning and decision-making
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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges
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or natural sciences Sound knowledge in machine learning algorithms, statistical methodologies, and biological network analysis Experience with the analysis and integration of transcriptomic and multiomics data