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methodologies. Experience with data analysis and computer programming in C/C++/Python, and ideally familiarity with ROOT and Geant4 A demonstrated ability to perform research individually and as part of a
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spectroscopy and particle detection techniques, including relevant instrumentation and data acquisition methodologies. Experience with data analysis and computer programming in C/C++/Python, and ideally
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group analyzing deep soil warming experiments across the world. In this exciting role, you will apply mechanistic and machine learning models to study observed effects of warming on soil and ecosystem
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and machine learning models to study observed effects of warming on soil and ecosystem carbon cycling and the mechanisms shaping the soil response, and to estimate potential soil carbon feedbacks with
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electron tomography data. Develop new data-driven methods that leverage physics-informed machine learning for reconstructing non-rigid deformations and generative modeling of conformational heterogeneity in
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. Author peer-reviewed journal articles and contribute to research proposals. Publish developed algorithms as software packages. What is Required: Ph.D. within the last 3 years in Machine Learning, Applied
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evaporation, use of simple photolithography, furnace annealing, and rapid thermal annealing (RTA). Test cell fabrication using computer-controlled machine tools. Quantitative analysis of PEC data, application
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chemical and physical principles along with an ability and eagerness to learn and apply new techniques. What You Will Do: Design and fabrication of electrodes and electrochemical devices using techniques
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. Ability to produce research deliverables on a scheduled timeline of milestones. Self-motivated and able to work independently. Ability to learn rapidly and integrate new skills. Willingness to take
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and will advance machine learning on near-term quantum computing platforms, and provide a foundation for algorithmic development by the broader scientific and engineering scientific community. What You