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- Delft University of Technology (TU Delft); Published 11 Nov ’25
- Delft University of Technology (TU Delft); yesterday published
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. Responsabilities: Investigate existing verification and validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations
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on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems
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distributed quantum computing. The center includes other quantum faculty, and conducts a wide range of collaborative quantum research in the areas of quantum computing, quantum algorithms and complexity
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North Carolina A&T State University | Greensboro, North Carolina | United States | about 1 month ago
with the application of Algorithms, Artificial intelligence (AI), and Machine Learning (ML) methods to accelerate material discovery and design, optimizing manufacturing processes in nanoscience
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have: experience with spiking neural networks and neuromorphic architectures; deep knowledge about our brain and how it makes decisions; coding reinforcement learning algorithms/feedback systems and
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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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11 Nov 2025 Job Information Organisation/Company CNRS Department Institut des Systèmes Complexes de Paris Île-de-France Research Field Computer science Mathematics » Algorithms Researcher Profile
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, the successful candidate will conduct cutting-edge research in: Adaptive incentive mechanism design for complex sociotechnical systems Strategic learning and equilibrium-seeking algorithms in transportation
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classification algorithms including machine learning); and the output data and interpretability. The project “SORS in the community” is funded by the EPSRC (https://www.ukri.org/news/new-tools-aim-to-improve-early
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: Design, implementation and testing of new methods and algorithms so that SIESTA can harness the compute power of the latest generation of (pre-)exascale architectures and tackle novel scientific challenges