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teams to codesign hardware, algorithms, benchmarks and software for QHPC systems, aiming to advance our strategic goals in leveraging quantum computing and high-performance computing (HPC) to develop
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applications of quantum networks along several possible lines: development of new quantum algorithms targeting various platforms, including qubit based and optical systems adaptation and generalisation of known
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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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-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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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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at NTU. Key Responsibilities: The successful applicant will be responsible for Geometric modelling for shape analysis and robotics Designing, implementing, and testing algorithms Engaging in scientific
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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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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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theoretical and algorithmic methodologies for learning latent representations shared across heterogeneous modalities, such as images, audio signals, text, and physiological data. Where to apply Website https