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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 17 days ago
usually reflect net sand transport over multiple Mars years). Estimations of sand flux rates are important for improved interpretation of all dunes and wind patterns around Mars, but also are used
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Uganda. Building and extending a decision-analytic (Markov) model to estimate the cost-effectiveness of telemedicine with self-monitoring and community medication delivery, relative to standard care, from
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new areas of basic and applied research Transition classroom theory into hands-on experience Network with world-class scientists Exchange ideas and skills with the Laboratory community Use state
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quantum magnetism and strongly correlated systems, as well as classical methods such as exact diagonalization, tensor networks or DMRG, and quantum Monte Carlo. Familiarity with inelastic neutron scattering
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or more of the following areas: (1) Generative AI and machine learning, (2) affective computing, (3) human-computer interaction or collaborative AI, and (4) interaction design, experimental design or
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. Preferred Experience designing studies in vehicle/driving simulators (fixed-base or VR-based). Unity3D development (C#), including multiplayer and networking (e.g., Netcode for GameObjects, Mirror, or Photon
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independently and the initiative to follow new ideas. Motivation to pursue career development opportunities (e.g. applying for Fellowships and Awards, promoting your research, networking, etc.). Strong verbal and
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust
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/ ). Together, CAHBIR, the CCNP, and a broad network of Rutgers-affiliated clinical partners provide an exceptional environment for training in clinically focused computational and cognitive neuroscience research
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complex dynamic system. Minor disruptions can lead to major delays with traffic jams spreading like oil spills over entire networks. We believe traffic management based on reliable predictions is therefore