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: - Distributed task and motion planning - Multi-agent navigation in constrained environments - Predictive collaboration with heterogeneous teaming - Perception-aware predictive motion planning - Distributed and
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with experimentalists to validate predictions made by their machine-learning models and drive wet-lab discoveries. The candidate may also have opportunities to work with research software engineers
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drive wet-lab discoveries, or to perform experiments themselves if they wish. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve
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. THE PROJECT: Membrane proteins make up one third of the human proteome. They are critical for any cellular function from movement to immune defense. Membrane proteins are major drug targets and their mutations
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for this Postdoctoral researcher position will focus on one or more of following key areas, where proof of previous work with experimentally verified results is necessary: - Distributed task and motion planning - Multi
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medicine within the Gurtner Lab. This position will drive independent and collaborative research efforts integrating molecular biology, animal models, single-cell sequencing, and spatial transcriptomics
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top choice for employment. These principles are essential for supporting our broader mission to drive scientific breakthroughs and translate them into solutions for energy, environmental, and security
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to deploy and scale, by running plug-and-play analytics inside modern drives or on a plant controller using only electrical signals already measured by the drive, while minimizing data export and supporting
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to identify metabolic alterations that drive tumor initiation and progression. This work has recently expanded to examine how metabolism and autophagy influence the biology of embryonic and cancer stem cells
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something different from standard ML roles. Rather than focusing only on performance, we aim to understand how models work and what they reveal about the brain. You will: Own your project and drive ideas from