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Field
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. Job Duties Prepares detailed specifications and algorithms from which Python and R programs will be written. The PI will lead in prioritizing goals and requirements. Implements, tests, debugs, and
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are looking for someone who can take ownership of a prototype from concept to a complete working hardware/software system, rather than focusing exclusively on simulation or algorithm development. Teaching
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localization, sensing, and tracking. This profile focuses on modelling, algorithm development, performance analysis, and signal processing for radio localization, sensing, and tracking in terrestrial and
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responsible for the end-to-end investigation of novel federated learning strategies for causal inference. The role will bridge rigorous theoretical work with hands-on algorithm design and development on real
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unsupervised machine learning algorithms, feature extraction, classification methods, etc. Rigorous experience in Python, C++, and MATLAB Knowledge of deep learning and big data Good understanding of probability
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C++ techniques. Solid understanding of algorithms, data structures, and memory management. Familiarity with debugging tools and performance profiling. Experience with version control (Git
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
(PETs) and privacy assessment functionality based on MAGNET scientific advances By default all developed software will be open-source. Tasks may include developing algorithms, e.g., cryptographic
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. Develop motion-planning, task-planning, perception, control, and feedback algorithms for operation in complex laboratory environments. Integrate robotic manipulators with cameras, force/torque sensors
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specialties including but not limited to quantum algorithms, quantum error correction, quantum computing architectures, quantum information, and quantum simulation of physical systems. Successful candidates
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Develop algorithms for reconstruction of virtual histopathology images from sparse phase-contrast CT acquisitions of oncological specimens Comparison with standard histology and assessment of algorithm