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underlying various biological networks. The systemic dynamics team aims to develop digital medicine for sleep disorders based on health-wearable devices via mathematical modeling and machine learning. In BIMAG
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: Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area: Applied Math Position Description: A postdoctoral position is available in the Geometric Machine Learning Group at Harvard
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humanistic questions. We anticipate that the Postdoctoral Associates will teach one seminar per year, which is one section of SHUM 2750 Introduction to the Humanities in the spring term. As well, we expect
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physical environments. This position focuses on research at the intersection of computer graphics, generative AI, and robotics, encompassing topics such as generative modeling, reinforcement learning, multi
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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-fidelity finite element models to investigate surface wave propagation in soft biological tissues, forming the foundation for subsequent statistical and machine learning frameworks that integrate
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 5 days ago
: 311391 Vacancy ID: PDS004746 Position Summary/Description: A position is available for a Postdoctoral Research Associate (RA) in the lab of Dr. Nicholas Brown, Department of Pharmacology and the Lineberger
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning frameworks (e.g., PyTorch, JAX) and / or computational materials methods is essential