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the environment, including traffic conditions, travel time, and cost. The project will define the DRL components (states, actions, rewards, policies), select and implement suitable DRL algorithms, and integrate
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Max Planck Institute for the Structure and Dynamics of Matter, Hamburg | Hamburg, Hamburg | Germany | 2 months ago
Experience in HPC computation (application and algorithm/code development) Willingness to closely collaborate with experimentalists and theoretician. Joint research approach of all ERC synergy team members
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of software interfaces and data models Selection and configuration of algorithms for annotating and organizing research data and software using state-of-the-art AI technologies Ensuring the sustainability and
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Your Job: Random unitaries are a ubiquitous tool in quantum information and quantum computing, with applications in the characterization of quantum hardware, quantum algorithms, quantum cryptography
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application from qualified women. About the position The position involves both teaching and engaging in innovative research projects on tractor autonomy, path-planning algorithms, soil compaction modeling, and
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of superconducting qubits to quantify performance and identify limiting physical mechanisms Perform quantum device calibrations, benchmarking, and run quantum algorithms Presenting and publishing the research
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experience with algorithms relevant to computational biology documented programming skills, e.g. in Python and R very good communication and organizational skills with the ability to work to timelines, both
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data Development of algorithms for infection and evaluation of infection hotspots in the plant population Coordination of the scientific interface to the project partners with regard to entomological
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. The project’s overarching goal is the development of digital quantum algorithms for the simulation of non-abelian lattice gauge theories. We are looking for highly motivated individuals, with the desire
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. Therefore, we employ a broad range of artificial intelligence algorithms that enrich process data with sophisticated domain knowledge and semi-supervised approaches to incorporate unlabeled data. We