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for interacting with colleagues and stakeholders. Department Specifics: Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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, Computer Science or related fields (for PhD); Doctorate in Physics, Computer Science or related fields (for Post-Docs). The positions are funded via the Cluster of Excellence (Machine Learning for Science), the ERC
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for interacting with colleagues and stakeholders. Department Specifics Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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and aggression, using optogenetics, in vivo imaging, electrophysiology, and sophisticated machine learning/artificial intelligence analyses of mouse behavior. All projects have translational components
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to three full-time Post-Doctoral Associate (9546 Post-Doctoral Associate) positions. For more info on the division, visit http://www.sph.umn.edu/academics/divisions/biostatistics/. The Post-Doc will work
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 6 hours ago
biogeochemical model using times series forecasting and machine learning. The Post Doc will focus on one or two of the questions depending on their expertise and interest. Minimum Acceptable Education & Experience
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required. Job Type: Full Time Rank: Post Doc Campus: Unionized Position Code: Not Applicable Job Description: The Mathematics Department at Syracuse University seeks to ll one 12-month Postdoctoral Scholar
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Position Details Position Information Recruitment/Posting Title Post-Doc Associate (Barr Lab) Department Human Genetics Inst of NJ Salary Details Offer Information The final salary offer may be
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Multi-Modal Artificial Intelligence and who have expertise on the topic of mental well-being. External and internal post-doc researchers who are eligible to submit an FWO or MSCA post-doc application
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the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships