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Field
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robotics – reinforcement learning, whole-body model predictive control (MPC), and differentiable optimal control – to simulate human balance and step recovery in urban transport scenarios. The goal is a
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intelligence - Data-driven and learning-based control - Decentralized decision-making and distributed optimization - Belief-space and uncertainty-aware planning - Neuro-symbolic and context-aware reasoning
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volumetric bioprinting to deposit functional layers inside the stent lumen, as well as computational simulations and perfusion bioreactor systems to optimize design and performance. The final goal is to
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motivation to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include: Robot modelling, Nonlinear and Optimal control
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Institute of Data Science, and Smart Grid Center, etc. Perform other duties as assigned. What we need: PHD Degree What is helpful: Ph.D. in Electrical Engineering, Computer Engineering, Computer Science, or a
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 15 hours ago
. Fluid Characterization will involve Pressure-Volume-Temperature (PVT) measurements, characterizing additives such as surfactants, optimizing additives, quantifying stability and reactivity of additives
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motivated post-doctoral associate (or research scientist for experienced candidates with 5 years of post-PhD experience). The candidate will work under the supervision of Future-GRID TecH director Dr. Mohamed
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community. Main Tasks and responsibilities: Collaborate with theoretical and experimental partners to design and characterize optimized altermagnetic devices for information processing. Fabricate nanodevices
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, characterization, model extraction and apply device model in the circuit design; Circuit design optimization; Using Cadence for circuit design and ADS for RF circuit design; Circuit implementation and