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analysis of atmospheric numerical model output (e.g., WRF, PALM, SAM) Experience with machine learning and artificial intelligence techniques Experience with predictive modeling Environmental, Health
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well as artificial intelligence and machine learning techniques (AI/ML) with emphasis on electronic properties (charge and spin) of a range of materials important to the DOE mission, including the materials classes
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Apply Now Job ID JR101405Date posted 09/12/2024 The Machine Learning Group of the Computational Science Initiative (CSI) at Brookhaven National Laboratory (BNL) invites exceptional candidates
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Apply now Job no:526348 Work type:Post Doc (Amherst Only) Location:UMass Amherst Department: CBIKS Union: Post Doc Categories:Postdoctoral Research Associate, College of Social and Behavioral
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Posted on Fri, 04/19/2024 - 15:02 Important Info Faculty Sponsor (Last, First Name): DeBoer, Charles Stanford Departments and Centers: Ophthalmology Postdoc Appointment Term: 1 Year, possible
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Position Title Post-Doc Research Associate Vacancy ID PDS004369 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Posting Information Posting Details Department Geography - 315800 Posting Open Date 06/24/2024 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title Post-Doc
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experience and enhanced potential to receive an ERC Starting Grant in the future. Open to both PhD (natural sciences) and MD (medical sciences) holders. From a variety of academic backgrounds: molecular
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, separation, and catalysis, with a focus on carbon capture and conversion technologies. Artificial Intelligence: Leveraging AI and machine learning to optimize material design and catalysis processes. Carbon
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning