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substantial intellectual independence and opportunities to collaborate closely with machine learning scientists developing predictive models of cellular function. Machine learning experience is not required
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statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
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Qualifications PhD Certificates/Credentials/Licenses n/a Computer Skills Microsoft Office Suite Supervisory Responsibilities No Required operation of university owned vehicles No Does this position require direct
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methods with the ability to implement and evaluate machine-learning systems at scale. Candidates may come from topological data analysis, geometric deep learning, network science, statistical physics
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Landscape analysis and modeling, with a focus on training and application of predictive machine learning tools. This researcher will synthesize regional-scale remote sensing imagery and ecological monitoring
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computing, high-performance computing (HPC), or machine learning (ML) Interest in Standard Model measurements and/or searches for new phenomena Application Review Review of applications will begin once the
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. Candidates will collaborate closely with interdisciplinary research groups advancing materials discovery through the convergence of computational chemistry, machine learning, and agentic science. This full
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. Conduct transportation resilience assessment and enhancement studies based on GIS, complex network analysis, and machine learning. Simulate human mobility in response to extreme weather events (e.g
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for high-dimensional dependent data, and data sketching approaches for massive data. Opportunities to Contribute: Develop statistical/machine learning methodology for multi-modal imaging data integration
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analysis, computer vision, or machine learning, with a clear interest in developing image analysis algorithms and an affinity with medical topics. Good communication and organizational skills are essential