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of two years. About the Project The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems
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-driven surrogate models for real-time reconstruction and forward simulations. Create numerical algorithms for physics reconstruction using sparse data. Implement assimilation pipelines which integrate
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; Strategic Human Resources Management; and Smart Ageing and Health Management. For those pursuing advanced research, CIE provides a PhD in Business, with concentrations in areas such as Industrial Innovation
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 22 days ago
to their field of expertise, contribute to the supervision of Honours and PhD students, work collegially with members of the ANU community, undertake suitable service tasks, and engage in impact and engagement
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, and development of algorithms for real-world clinical data. The fellow will have the opportunity to work with clinicians, engineers, data scientists, trainees, and research staff in a highly
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University of California San Francisco | San Francisco, California | United States | about 2 months ago
interpretation algorithms. A primary application will be segmentation and quantitative analysis of PSMA PET imaging in prostate cancer, with additional projects spanning radiology-based AI applications across
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Qualifications · PhD or MD/PhD in neuroscience, molecular/cell biology, bioengineering, or a related field. · Demonstrated experience in extracellular vesicle exosome biology and advanced statistical analysis
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design, and algorithms. The plan concludes with the students fulfilling their core requirements, a senior design capstone project, and a music recital that has a technology component. This plan will
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systems. Examples of research topics include advanced motor control using digital control algorithms (e.g., model-predictive control, adaptive controllers) and their implementation on digital platforms (e.g
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involving Prof. Dr. Michael Bader (TUM CIT, Hardware-aware algorithms for HPC) , Prof. Dr. Felix Dietrich (TUM CIT, Physics-enhanced Machine Learning) , and Prof. Dr. Hartwig Anzt (TUM CIT, Computational