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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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approaches for multivariate Climate Extremes modelling. To identify and exploit applications of Machine Learning to extreme values. To produce scientific material (papers/articles, conferences, seminars
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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
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or oceanography. Research background should demonstrate competence -- or at least a clear and strong interest -- in artificial intelligence and machine learning to be applied in the field of environmental sciences
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. Carry out the experiments and analyses required to complete the project, which aims to evaluate the role of Golgi cells, their inputs and outputs during the emergence and learning of postural adaptation