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. This position is ideal for candidates with strong training in molecular biology, developmental biology, neurobiology, or physiology, and an interest in method development and comparative biology. The candidate
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) at Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 18 hours ago
infrastructure such wells and reservoirs. The selected participant will conduct research in close conjunction with NETL federal and contract staff to: (1) test and validate open-source methods for extracting
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. Proficiency in R (+SAS or Python a plus), and experience working with quantitative data analysis and comprehensive understanding of statistical concepts and methods are essential. Certifications/Licenses
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science, energy engineering or a closely related field. Solid background in thermodynamics, transport phenomena, and process modelling and computer programming with documented experience in developing and applying
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various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly
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of data developed in two Horizon Europe funded projects we coordinate: Climate Action To Advance Healthy Societies in Europe (CATALYSE, GA 101057131) and the Urban Burden of Disease Estimation for Policy
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chromatographic conditions. Development and validation of extraction methods and passive sampling approaches at laboratory and field scale. Development and implementation of extract purification protocols
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at the intersection of climate-related exposures and worker health under the mentorship of Dr. Lee Newman. The candidate will use epidemiological methods to research the impacts of extreme weather conditions and social
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, uncertainty-aware parameter estimation, surrogate modeling, and reduced-order modeling where relevant. The scientific emphasis is mechanics-first: we are looking for someone with strong foundations in nonlinear