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Job Description Job Responsibilities: -Design and implement AI/ML models to analyze large-scale agricultural datasets (e.g., field trials, satellite imagery, IoT sensor data). -Develop pipelines
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Algorithms. The candidate is expected to conduct research in computer science focusing on the combinatorial aspects of quantum experiments and quantum algorithms for computational geometry problems. Prior
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for ground/space segment threat collection via existing and novel interfaces, including multi-source sensor intelligence collection, fusion and dissemination. 5) Applied forensics for space missions
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 3 days ago
Requirements Specific Requirements This project will require a candidate with experience in: Image registration algorithms (non-rigid is a plus) Advanced Python programming skills (especially with Torch / Keras
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several
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Responsibilities: Digital Tool Development:Develop digital tools to monitor and analyze the determinants of urban health using sensor data, public databases, and GIS. Predictive Modeling:Design predictive models
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manufacturing - smart sensors, digital twins, and AI-driven food quality control and processing; Food safety - supply chain tracking/monitoring, blockchain Biotechnology: Host strain screening and engineering for
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. university groups within CMS. Our instrumentation expertise spans detector operations and upgrades (Level‑1 muon trigger, CSC muon system), tracking algorithm R&D, and future HL‑LHC trigger algorithm
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environment project, we will develop automated species and community recognition, particularly focusing on pathogenic soil fungi, with help of deep-learning algorithms fed with microscopic image and Raman