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, thermal management, and energy conversion. We seek candidates with strong expertise in building and conducting ultrafast time-resolved optical experiments. Key skills include the ability to design, assemble
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-fabrication and knowledge in molecular biology, diagnostics, and/or optical measurements is a plus.2. Nanofabrication and applications. Candidates should have significant experience in micro/nano-patterning
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the delivery of clean energy and industrial decarbonization infrastructure associated with net-zero transitions. The role will report to the Andlinger Center's Dr. Chris Greig, the Theodora D. '78 and William H
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postdoctoral research associate position, to start as early as September 2025. The Ferris group studies high-temperature reaction chemistry and particulate formation using optical diagnostic methods, with
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. Candidates should demonstrate track-records in microfluidics, Lab-on-Chip, and micro-fabrication and knowledge in molecular biology, diagnostics, and/or optical measurements is a plus. 2. Nanofabrication and
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September 2025. The Ferris group studies high-temperature reaction chemistry and particulate formation using optical diagnostic methods, with applications to alternative fuel design and atmospheric chemistry
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candidates with strong expertise in building and conducting ultrafast time-resolved optical experiments. Key skills include the ability to design, assemble, and align ultrafast optical setups, integrate setups
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to excellence in education are encouraged to apply. A PhD in Materials Science, Optics, Physics, Chemistry, Electrical, Chemical, Mechanical, Civil or Bio Engineering or related area is required. We
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation increments, which represent structural model errors (https://doi.org/10.1029/2023MS003757
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models, programming, and quantitative methods. Preferred qualifications include experience in reinforcement learning, neural networks, and/or statistics. Questions can be addressed to Professor Nathaniel