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computation, probabilistic machine learning, latent-variable models, unsupervised learning, or matrix and tensor factorization is an advantage. Experience with computational methods for large or high
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/Anders Lien 16th October 2026 Languages English English English PhD Research Fellow in Machine Learning and Statistics Apply for this job See advertisement About the position Integreat - the Norwegian
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deployment enabling validation and demonstration of real-world applications. For more details, please view https://www.ntu.edu.sg/erian We are looking for a Research Associate to conduct numerical modelling
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Prof. Igor Zozoulenko). Our focus is on: Machine-learning accelerated materials simulations ML-accelerated simulation of ion and charge carrier transport in energy materials Multiscale modeling
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(e.g., ROS, MoveIt) and hardware integration. Knowledge of machine learning, reinforcement learning, or vision-language models for robotics is a plus. Hands-on experience with robotic arms (e.g., UR5
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Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
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essentials, data structures and/or databases, statistical computing and machine learning. Experience: Experience within an academic environment. Experimental design including basic science experiments
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Join the Responsible Machine Learning (ML) Group at the Faculty of Computer Science. Led by Prof. Dr. Martin Pawelczyk, who recently joined the University of Vienna from Harvard University, our research
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interconnected and closely intertwined scientific themes. The first targets the development of hybrid algorithms combining multi-physics modelling of electronic components, predictive control and machine learning
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evidence to support the evaluation of AI and machine learning models. This may include investigating data-centric AI strategies, such as data quality assessment, annotation refinement, dataset curation, and