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6 Jan 2026 Job Information Organisation/Company Université Gustave Eiffel Department Navier Research Field Physics » Classical mechanics Engineering » Simulation engineering Researcher Profile First
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algorithms Extend the superstructure to tackle AC-PF problems of different complexities and assess its convergence in inference Investigate scaling and performance bottlenecks Explore hybrid ML-classical
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other departments and programs at Hamilton, including American Studies, Cinema and New Media Studies, Classics, and Sociology. Candidates with ABD will be considered, although candidates with a Ph.D. are
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and field deployable. The accelerometer will be tested in the field on moving vehicles, in combination with a classical accelerometer. The aim of this position is to develop a new exciting accelerometer
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, scattering amplitudes and their applications to classical gravitational systems. Review of applications will begin in January 2026 and continue until the positions are filled. For more information, please
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into actionable insights, novel tools, and impactful research outcomes. Key Responsibilities Develop, implement, and optimise AI/ML models (artificial intelligence/classical machine learning, deep learning
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different tumor cell subtypes in pancreatic adenocarcinoma (in particular the basal and classical phenotypes), using bulk spatial transcriptomics data from the ITMO Cancer ACACIA project and public datasets
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Gorlitz, Sachsen | Germany | 2 days ago
implementation of advanced algorithms, including variational and optimization-based approaches # Investigation and adaptation of mathematical methods in hybrid classical–quantum computing contexts # Documentation
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, Quantum Transport in topological phases , Statistical physics of classical and quantum systems, Theoretical condensed matter physics, Field theory, Quantum information theory, Theoretical Physics
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engineering, or similar. Solid mathematical and analytical skills, including signal processing and optimization. Knowledge about classical and/or quantum data communication, including for instance error