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for peer reviewed publications Qualifications*Ph.D. in Environmental/Civil Engineering, Computer Science/Engineering, Data Science, or a closely related field*Proficiency in Python or other tools and ML
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, materials science and engineering, chemical engineering, or in a relevant engineering field, with an extensive background and training in the operation of a wide range of spectroscopic and imaging techniques
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candidates with a background in political science, economics, modern history, sociology, anthropology, law, business, and other disciplines bearing on the study of globalization to apply. The postdoctoral
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transport, buildings, and industry sectors. *Experience with managing, processing and analyzing large datasets. *Strong programming skills, particularly Julia/JuMP and/or Python. *Strong scientific writing
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: 278716346 Position: Postdoctoral Research Associate Description: The Atmospheric and Oceanic Sciences Program at Princeton University, in association with NOAA's Geophysical Fluid Dynamics Laboratory (GFDL
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. Candidates who are nearing completion of their Ph.D. (i.e. with a confirmed defense/viva date) or hold a Ph.D. in chemical engineering, materials science and engineering, chemistry, physics, or a closely
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record of research and publications related to the job descriptionStrong scientific writing and communication skillsExperience with Robot Operating System (ROS)Excellent programming skills (Python is
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on the scientific, technical, policy, and human dimensions of environmental issues. These areas include issues surrounding global change; energy and climate; biogeochemical cycles; molecular geochemistry
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are of relevance. Outstanding scholars anywhere in the world are eligible to apply. NCGG invites candidates with a background in political science, economics, modern history, sociology, anthropology, law, business
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subdivisions of the squamate body plan. The candidate will work towards developing computational resources that assist in the data management and analysis of genomic data and its integration with phenotypic data