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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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-learning prediction models” with the following focus areas: Design and development of methods for drifter detection in self-learning AI models Evaluation using real data sets from photovoltaic systems and
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, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and automatic code optimization
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the REACT Doctoral Network. In this context, we seek a highly motivated researcher with a proven track record in parallel programming models for CPS, high-level compilers, system and computer architecture and
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planning and control algorithms Multi-modal perception techniques (e.g., vision, tactile, force) Machine learning models for physical behavior prediction and manipulation strategy adaptation Real-world
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), and computational modeling (deep neural networks). We apply multivariate analysis methods (machine learning, representational similarity analysis) and encoding models. Job description: This is an open
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computational models with the "exact" but lower resolution information available from experiments. Job description: - Research and teaching is done on statistical physics and machine learning in physics
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systems using various tools and models, including: i) characterization of the emerging patterns in physical systems (solid state materials and active systems); ii) investigation of the mechanical properties
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architectures, capable of capturing the structure of complex, high-resolution NMR spectra – analogous to how language models such as ChatGPT learn the structure of human language. One of the primary goals is to