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molecular simulations, machine-learning techniques, and statistical mechanics for research opportunities in: Development of data-driven schemes for the discovery of slow degrees of freedom Molecular
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Postdoctoral position (M/F): Machine learning design of alloys for concentrated solar energy storage
mission will be to develop machine learning models to predict properties of alloys of elements of groups 1 to 15, such as their melting temperature, range, and enthalpy. Based on these predictive models
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approach and framed as a continuous improvement process, and (3) on machine learning algorithms guided by theory and analogues from natural objects and simulations. The proposed position will cover four
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of learning dynamic systems and physically informed neural networks (PINNs), for application to neuroscience research. The main task of the postdoctoral fellow will be to develop models for modeling
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., Nature Communications (2020) 11:4691] based on an analysis of local atomic environments using “machine learning” methods (MiLaDy). In parallel with this analysis of the database, and to have a better idea
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. Fujii, K. & Nakajima, K. Harnessing disordered-ensemble quantum dynamics for machine learning. Phys Rev Appl 8, 024030 (2017). 2. Rudolph, M. S. et al, Generation of High-Resolution Handwritten Digits
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, deep learning-based image methods have emerged as a prominent tool in medical image processing. While they have shown impressive success in various computer vision tasks, their application in the medical
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physics or computer science, with a solid background in AI/machine learning techniques. A background in plasma transport phenomena as well as an experience with data analysis, statistical methods, and
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materials or molecular modeling and will have skills in algorithmic programming (python required, C++ would be a plus). Experience in the field of machine learning will be appreciated. Additional comments
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or as materials for transportation. Intensive calculations within the framework of density functional theory (DFT) will provide the basis for building machine-learning models to explore the range