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a PhD’s degree in the area of power and energy systems. Good experience in one or more of the following topics: power system stability and control, power system modeling and simulation, inverter-based
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, mechanical engineering, or related field. Research experience in robot manipulation, robot learning (imitation, reinforcement learning and /or foundation models) and/or robot perception (vision, depth, touch
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model for communities worldwide facing similar 21st-century challenges. This is a fixed-term position for one year with the option to renew for a maximum of two years. UT Austin offers a competitive
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of decarbonization policy pathways for electric power systems. Desired experience includes optimization and simulation of electric power systems under uncertainty, programming in Python, Julia, and/or MATLAB, and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
modeling and bioenergetic simulation. The position is funded for an initial period of 1 year. However, we are deeply committed to the training and professional development of our post-docs, and intend
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dynamics enabling naturalistic animal behavior? Our labs aim at building mechanistic models of brain function grounded in a combination of theoretical approaches, neural network-based simulations, and
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. Active research thrusts within the AiPEX lab include AI for complex physics-based modeling and simulation, AI for enhancing human performance and decision making, AI for advancing personalized learning
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Adaptation Postdoc (SCCAP) will be tasked with building agent-based simulation models (ABM) from current knowledge of bird and insect migration. They will then use these ABM to evaluate how such species
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
AIMD simulations and develop machine learning methods to accelerate the design of materials applied to optoelectronic devices and quantum information. This position offers exciting opportunities
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in Agriculture • Machine learning models for pest and disease prediction • Crop classification using multispectral imagery • Digital twin models for farm simulation and management • Collaborate with