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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), seeks a postdoctoral or more senior research scientist to develop hybrid models for sea ice that combine coupled climate models and machine
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injection, ion transfer, and structural dynamics in realistic and model systems for battery materials. The position will span experimental efforts at large scale X-ray facilities, handling and reduction
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receptor signaling. Our lab uses a variety of biophysical techniques to characterize protein structure and dynamics in these studies, notably continuous-wave and pulsed EPR methods such as DEER spectroscopy
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theoretical and practical experience with machine learning methods, especially for training of machine learning potentials - development and utilization of ab initio electronic-structure methodology - previous
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and laboratory-based computed tomography (CT). By combining in situ and ex situ analyses, the study will reveal the structural evolution, crack formation, and interfacial dynamics of silicon anodes
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that enhance the performance and energy flexibility of residential and commercial buildings. Your main responsibilities will include: Development of data-driven dynamic models to improve energy flexibility in
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SPECIFICS Penn State invites applications for a full-time Postdoctoral Scholar to join a dynamic, federally funded ARPA-E project focused on geologic hydrogen production. The project explores the potential
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). The successful candidate will contribute to the REINS research project, focusing on runtime reconfigurable embedded AI solutions for heavy construction machinery. Application deadline: September 15, 2025
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and has become one of the most dynamic, complex and exciting areas of research in both food and pharmaceutical arenas. Over the last two decades APC Microbiome Ireland has established itself as one
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
computational chemistry to MFMs is that, even for "rigid" MFMs, especially when functionalized, their structures are disordered due to the random orientation of linkers. Such structural disorder and flexibility