3,094 phd-position-in-data-modeling-"Prof"-"Prof" Postdoctoral positions in United States
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monitoring and gradient tests. Participate in training opportunities, including attending the US Particle Accelerator School (USPAS). Position Requirements PhD completed in the past 5 years or soon to complete
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Application’ in the subject line of the email. Does this position pay above the required minimum?: No. The expected base pay for this position is the Stanford University required minimum for all postdoctoral
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Materials: Submit application materials to Leigh Wang, lzwang@stanford.edu (link sends e-mail) Does this position pay above the required minimum?: No. The expected base pay for this position is the Stanford
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Experience Appropriate PhD in neuroscience, pharmacology, or related areas. If the degree had not been conferred at the time applicant applied for a postdoc position, a letter needs to be attached from
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
Posting Information Posting Details Department Radiology - Research - 412802 Posting Open Date 09/30/2025 Application Deadline Open Until Filled Yes Position Type Postdoctoral Scholar Position Title
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Fellowship - Bohg" Does this position pay above the required minimum?: Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected candidate will be
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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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Postdoc Fellowship - Kochenderfer” Does this position pay above the required minimum?: Yes. The expected base pay range for this position is listed in Pay Range field. The pay offered to the selected
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, execution, and data interpretation) Numerical modeling and simulation to complement physical experiments Preferred) Proficiency in CAD and modeling tools such as SolidWorks for experimental design and
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation