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SD- 26053 PHD IN ULTRA-FAST MACHINE-LEARNING INTERATOMIC POTENTIALS FOR NANOINDENTATION OF TIC MA...
PhD candidate to develop and apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular
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apply ultra-fast machine-learning interatomic potentials (UFPs, Xie et al., npj Comput. Mater., 2023, 10.1038/s41524-023-01092-7 ) for long, multi-million-atom molecular dynamics (MD) simulations
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, Reasoning and Validation (Serval) research group and work on a research project related to the application of machine learning for official statistics. The subject of the thesis will be “Exploring Large
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aspects of machine learning focusing on efficiency, generalization, and sparse neural networks. Currently we are expanding our expertise by applying our theoretical findings also to robotics. Hybrid is our
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generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research directions and
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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Machine Intelligence (CVI²) research group (CVI² Group ), led by Prof. Djamila Aouada, to pursue a PhD in Computer Vision with a focus on Media Forensics and Deepfake Detection. The candidate will conduct
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Machine Intelligence (CVI²) research group (CVI² Group ), led by Prof. Djamila Aouada, to pursue a PhD in Computer Vision with a focus on Media Forensics and Deepfake Detection. The candidate will conduct
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Machine Intelligence (CVI²) research group (CVI² Group ), led by Prof. Djamila Aouada, to pursue a PhD in Computer Vision with a focus on Media Forensics and Deepfake Detection. The candidate will conduct
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develop machine‑learning models that learn from and build upon these pNTA results. The successful candidate will be supervised by Prof. Dr. Emma Schymanski and Dr. Federica Piras. For further information