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Learning algorithms (clustering algorithms (K-means), genetic algorithms, and reinforcement learning) and end-user piloting of software systems – including technical attributes and legal/commercial
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Fields Institute Fellowship ID: FIELDS-FPDFQUANTUMALGORITHMS [#27996] Fellowship Title: Fields Postdoctoral Fellowship - Quantum Algorithms for Differential Equations Fellowship Type: Postdoctoral
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optimization of advanced reconstruction and control algorithms, combining numerical modeling, inverse problem techniques, and AI-driven approaches, together with experimental validation to improve imaging
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reducing waste, product failures and emissions. For this master’s thesis you will join our Competence Unit Complex Dynamical Systems , which focuses on the development and deployment of algorithms to control
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, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted to complex 3D underground geometries. The research will be conducted in close
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algorithms as well as deep learning workflows on GPU servers (use of Git, Docker, and PyTorch) Design, implementation, and evaluation of spatial proteomics and multiplex analyses for characterizing the tumor
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-aware AI, HR analytics and logistics, optimisation techniques for labour and warehouse management. Experience of applying Machine Learning algorithms (clustering algorithms (K-means), genetic algorithms
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are offering two distinct projects for this 10-week internship period. Both projects focus on the Tergite software stack, which enables the execution of complex algorithms on our 25-qubit processor
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(scRNA-seq) data, and structural data from cnidarians, and we will develop new algorithms to analyze the evolutionary history of muscle components. You will study the evolution of muscle components during
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processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large