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Field
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frameworks, including quantum optimal control, stochastic wavefunction methods, and Floquet theory, to design electromagnetic waveforms that can selectively discriminate and disrupt proposed spin-based quantum
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University Abu Dhabi invites applicants to apply for the open Post-doctoral Associate position to perform primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control
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structure, dynamics, and switching mechanisms at the nanoscale, including optical manipulation of polarization states. Design and execution of experiments demonstrating optical control of ferroelectric
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holder will lead research activities centred on reinforcement learning, optimal control, and simulation-based policy development, using robotic and biomechanical simulation environments to design and
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++) for microcontrollers, as well as familiarity with automation tools (e.g., LabVIEW, PLC) and version control systems (e.g., Git). Additionally, they should showcase experience in prototyping, debugging, and testing
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-driven frameworks for multi-scale modeling, multi-objective optimization, and predictive control of complex chemical and biochemical processes. The research will contribute to next-generation smart
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Science, or a related field. In-depth research experience and solid expertise in one or more of the following areas: Advanced Process Control and Optimization Digital Twin and Modeling & Simulation Predictive
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of neuronal and vascular responses; Contribute to the instrumental optimization of the imaging system (detector configuration, illumination control, multi-camera synchronization); Analyze and interpret
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groundbreaking research. The project is devoted to learning-based control for high-dimensional data. Application to individually optimized immunotherapy for cancer will also be considered. The grant covers two
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motivated Postdoctoral Researcher to join our Grid Planning and Analysis Center (GPAC). This position will focus on tackling critical optimization challenges in medium-term power system planning like