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one or more of the following areas is a BIG PLUS: data science (machine learning and AI), cancer biology, animal physiology, organic chemistry, E3-ubiquitin biology, and gene editing. In all cases
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 16 days ago
Post-doctoral Position In Medical Image Processing The Computer-Assisted Medical Interventions (CAMI) team at the TIMC laboratory (Grenoble, France) is seeking a highly motivated postdoctoral researcher
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field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency
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Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a
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Lightweight and flexible solar cells Space deployable structures Device analysis in space environments Big data, AI, and machine learning for space solar initiatives Outstanding Postdoctoral Training Strategy
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statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results at consortium meetings
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, and robotics. ARG's research interests include topics such as robot learning, human-robot interaction, Generative AI, computer vision, closed-loop control, extended reality (XR), and computational
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discipline expertise and computational areas, such as data science, artificial intelligence (AI), machine learning (ML), generative AI and other technology innovations. Key details of this position include
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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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of Science at UvA. What are you going to do? The aim of the project is to use advanced Machine Learning techniques to predict the anharmonic vibrational spectra of large Polycyclic Aromatic Hydrocarbon (PAH