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++, or similar, with experience in data-driven workflows and computer vision Demonstrated track record of peer-reviewed publications Highly collaborative, innovative, and capable of working independently in a
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techniques in interfacial science; and mathematical techniques and computer programming for data analysis. Considerable skill in working interactively and productively in a multidisciplinary environment Good
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. The position will require flexibility and a willingness to learn new techniques and approaches. In addition, there may be overnight experiments being run unattended, the candidate must be able to respond
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methodologies and tools for economic and ecological analyses of hydropower systems. The position will involve the development and use of computer models, simulations, algorithms, databases, economic models, and
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experiments. Develop reinforcement learning models to improve gate fidelity. Leverage CNM’s state-of-the-art facilities, including the nanofabrication cleanroom and the Quantum Matter and Device Lab’s dilution
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of photosensitizers and solar fuels catalysts to be interrogated in-situ, under operando conditions and with atomic-scale resolution. Position Requirements The successful candidate will be highly motivated and have a
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), and cell free methods. Key Responsibilities: Development and optimization of vector constructs and expression condition characterization of protein yields and quality, and large-scale protein production
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will facilitate the comprehensive characterization of microelectronics under various conditions, including thermal, mechanical, and radiation stresses. The software developed through this project will
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reports for sponsors, and attend and make presentations at scientific meetings Communicate effectively with supervisors, peers, and Laboratory management through status updates, technical research reports
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing