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Previous Job Job Title Post-Doctoral Associate Next Job Apply for Job Job ID 369049 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular Job Code 9546 Employee
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paradigms, neural circuit mapping, advanced genetics and two-photon calcium imaging to uncover how the brain selects between conflicting drives, and how these choices vary by context, internal state, and sex
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Max Planck Institute for Astronomy, Heidelberg | Heidelberg, Baden W rttemberg | Germany | 11 days ago
Job Code: 395 Job Offer from June 26, 2025 The Max Planck Institute for Astronomy (MPIA) in Heidelberg invites applications for up to three postdoctoral position to work on topics related to ISM
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
imaging, and structured electronic health record (EHR) data. The postdoctoral researcher will work closely with the Director for Cardiovascular Artificial Intelligence Research and contribute to the design
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plan to dedicate a up to 2 nights a week to night scanning (typically between 7-11pm) Mentorship and Laboratory Responsibilities (10%) Undergraduate mentorship (data entry, coding projects, image
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Code 9546 Employee Class Acad Prof and Admin Add to My Favorite Jobs Email this Job About the Job Compensation Range: $62,232 – 75,564.00 annually; follows NIH stipend level by experience Work
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implementation Reference code: 50144551_2 Commencement date: 01.09.2025 Work location: Hamburg Application deadline: 13.07.2025 The Climate Service Center Germany (GERICS), a scientific organizational entity of
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holographic optogenetics and calcium imaging in awake head fixed mice, to understand how groups of neurons work together to enact motor actions. Neural activity is complex and high dimensional with some
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Previous Job Job Title Post-Doctoral Associate Next Job Apply for Job Job ID 368611 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular Job Code 9546 Employee
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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with