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Assistant. This position will work with research projects pertaining theoretical computer science, specifically graph algorithms and fine-grained complexity. Analyze, record and assess data. Write and test
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coordination across multiple laboratories to keep device and validation activities on schedule, with secondary support to upstream sensor/assay teams. You will help drive a multi-year, multi-disciplinary program
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Design and apply deep reinforcement learning algorithms for online HVAC control in greenhouses and for data mapping with multi-robot systems. Implement and test algorithms in simulation and
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project that involves designing microfabricated biomedical sensors and the readout circuitry for recording electrophysiology and electrochemical signals from biological samples. The overarching goal
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of data collected and writing up experiment results. Core Responsibilities Working with and analyzing biological data Working with large-scale biological data Developing, implementing algorithms
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considered. You should demonstrate: A strong foundation in micro- and nanofabrication, materials and surface modification, and sensor integration, along with competence in electronics, data acquisition, and
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progress in machine learning and artificial intelligence, the successful candidate will have primary responsibility to develop, implement, and test multimodal machine learning algorithms to analyze and
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reliability of robotic systems. In this role, you will design and implement a safety monitoring system for robotic platforms, utilizing advanced algorithmic tools to validate and enhance the performance
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Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods of research, testing and data
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. Modeling dynamical systems Designing and extending algorithms grounded in probabilistic machine learning Applying statistical techniques to assess robustness and generalization. Development of methods