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predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a universal representation of local interactions (fluid-structure, robot-robot, robot-object
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at the surface and interact with meteoric fluids, allowing for the direct observation of active serpentinization and hydrogen generation. A joint active-passive seismic experiment, including controlled water
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the problem intractable. However, in many systems the interactions are not random (they have some structure) and are often short-ranged, which allows to dramatically reduce the effective number of degrees
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(ILM), interacting with PhD students, postdocs, and master's students on related topics. We seek a motivated candidate holding a master's in physics, with skills in ultrafast/nonlinear optics or soft