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systems Reinforcement Learning and Agentic Control: Hands-on experience with reinforcement learning, multi-agent systems, or planning-based agents for autonomous vehicles or robots operating in dynamic
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and engineering optical setups Experience with coherent control of quantum systems Competence in electronics design and hardware control Ability to acquire and process large datasets Enthusiasm
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of experiments or projects under control or supervisory responsibility. 2. Recommends changes in research, testing or experimental procedures. 3. May be responsible for a single highly technical and complex piece
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field. Solid knowledge, and independent research capability in optimization, computing, power system engineering with track records of publications. Proficient in implementing control and optimization
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high-fidelity qubit control and further ingredients for a shuttling-based architecture, using devices from academic or industrial fabrication. Key activities can include: Demonstration and experimental
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of large-scale systems. The ideal candidate will have a strong foundation in operations research, control theory or power systems and a passion for applying these skills to real-world energy problems
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infrastructure; Development and implementation of electro-mechanical quality-control and testing procedures for detector validation; Development of data acquisition (DAQ) systems for silicon tracking detectors
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, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results into applications in close collaboration with industrial
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make a difference in the world! Position Information Texas A&M AgriLife Research – Dallas Center (https://dallas.tamu.edu/ ) is seeking a highly motivated individual to join the Controlled Environment
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neurodegenerative diseases Our laboratory has discovered two previously unrecognized protein quality control (PQC) systems involving tripartite motif (TRIM) proteins and poly-Asp/Glu (polyD/E) proteins