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signals, imagery, and natural language, via adaptive machine learning; 2) Personalization of sequential decision making, biophysical digital twins, and the operation of digital and physical systems; and 3
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substantial independence, collaborate with clinicians, engineers, computational scientists, mentor junior trainees, contribute to manuscripts and grant applications, and develop toward an independent research
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Postdoc: Machine learning for wind flow prediction in coastal dunes Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
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and meteorological modeling. Experience running or maintaining operational/automated forecast systems. Experience applying machine learning methods to air quality or geophysical data (e.g., forecast
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Experience developing software with Python, R, and/or Julia Experience with machine learning and AI algorithms and tooling (e.g. PyTorch) Experience with remote sensing and open-source GIS tools (e.g. Google
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Demonstrated experience in control theory and control system design (e.g., model predictive control, feedback control, adaptive control) Experience applying machine learning/AI to engineering systems Proficiency
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
motivated candidate with a strong interest in machine learning and clinical neuroscience. The lab studies resilience and vulnerability in brain aging and dementia, and builds computer-aided methods and tools
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before starting—in computer science, electrical or computer engineering, information science, or a closely related discipline. Less than 5 years post receiving the Doctoral degree. Meet the mandatory
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international