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Field
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for diagnosis and prognosis of different sarcomas. He/she will develop and train deep learning models with state-of-the-art algorithms based on histology whole slide images. They may also contribute to research
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simulation tasks. A key objective will be the development and testing of algorithms for gas hydrate phase equilibrium calculations. CapSim seeks to advance CO₂ capture simulation technology by enhancing
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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2 Oct 2025 Job Information Organisation/Company CNRS Department Laboratoire d'Informatique et des Systèmes Research Field Computer science Mathematics » Algorithms Researcher Profile First Stage
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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | about 6 hours ago
, and C/C++ Experience developing machine learning algorithms Strong mathematical foundation specifically in linear algebra, differential equations, and optimization Solid communication and teamwork
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quantum dots. In particular, based on our recent result in small arrays, we aim at exploiting coherent control of spin qubit in larger arrays to perform quantum simulation or algorithm. Therefore
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
optimization algorithms Extensive communications protocol design Solid understanding of physical/link layer communications, including synchronization, beamforming, and autonomous rate adaptation Point of Contact
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on are: • approximation algorithms in QIT (e.g. ground state energies); • symmetry- and size reduction of polynomial optimization problems; • characterization of quantum codes and quantum capacities
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods