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Field
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. The position is part of a large initiative funded by KAW to develop a comprehensive platform that will enable, for the first time, deterministic control of atomic configurations in UWBG materials. The project
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trajectories and enhance interaction safety and robustness. Learning for Human-Robot Interaction: Integrate machine learning techniques, such as reinforcement learning and generative models, with control theory
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, synthetic biology, yeast genetics, or control theory is a plus but not necessary. Ph.D is required. For more information, please visit: https://scholar.princeton.edu/jlagroup. Princeton University is
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to improve our research, and (ii) exemplary passion and motivation to enhance our multidisciplinary research at the intersection of control theory and machine intelligence. Methodologies of interest include
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. Preferred Qualifications Experience with: C/C++, Python, MATLAB, ROS 1 and 2, OpenCV, Unity, GPU programming, linear and nonlinear control theory, supervised, unsupervised and reinforcement learning, Torch
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activity patterns being associated with movements and others not. By using our novel techniques, that can drive precise patterns of activity distributed across many neurons, we can test different theories
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proposals to existing and prospective sponsors. Knowledge, Skills and Abilities: Knowledge on theory on free space optical communication, air turbulence and adaptive optics correction. Familiarity with
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of the tissue microstructure. In this position, you will work on developing advanced preclinical MRI techniques and you will employ them for 1) in-vivo mouse liver imaging, both for normal controls and in models
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scholarship is an ideal starting point for someone wishing to establish a future cross-disciplinary research lab that integrates theory and experimental research. Specific Qualifications for Project A
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of the planning and implementation processes with emerging energy technologies. You will develop and execute trainings and workshops for practitioners based on theories in Environmental psychology and empirical