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Field
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lead the development of the new proposed NC-ARPES technique and will also have the opportunity to propose new and independent investigations with the state-of-the-art TR-ARPES machine at ALLS
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. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome profiling, or CAGE data
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microphysics, severe storms, and mesoscale meteorology; data assimilation, machine learning, and causal discovery; education and human resources; global biogeochemical cycles and ecosystems; and radiation and
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, and AI chatbot chats; (b) quantitative content analysis; (c)text mining and machine learning methods; (d) survey design and public opinion research; (e) election studies; (f) the Norwegian political
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English
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Staff Union Non-Union Non-Classified Staff Pay Grade Level N/A Pay Grade Range N/A Status Calendar Year, Full-time, Limited Department Information Department Elec, Computer & Bio Engr Contact(s) Please
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Control Lab at Harvard University, under the direction of Dr. Maurice Smith, is seeking a postdoctoral fellow to study human motor learning and motor control. The lab is in the Harvard Paulson School
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technology. Key Responsibilities: Collaborate with partners from both the academia and the industry to lead and/or conduct innovative research on, but not limited to transfer learning, explainable machine
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain