81 machine-learning "https:" "https:" "https:" "https:" "https:" "IMT Atlantique" "IMT Atlantique" Postdoctoral research jobs at Duke University
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, United States of America [map ] Appl Deadline: (posted 2025/09/04, listed until 2026/02/20) Position Description: Apply Position Description Postdoctoral Associate – Scientific Machine Learning for Multiscale Biological
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, United States of America [map ] Subject Areas: Mathematics / applied mathmetics , Mathematical Sciences , Partial Differential Equations , Statistics Machine Learning Computer Science Appl Deadline: none (posted 2025/08
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, or military status. Duke aspires to create a community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of ideas—an exchange that is best when
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background and interests and contact information only for at least three references to AcademicJobsOnline.org https://academicjobsonline.org/ajo/jobs/31488. ; We will begin to review applications on February
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conditions), sexual orientation, or military status. Duke aspires to create a community built on collaboration, innovation, creativity, and belonging. Our collective success depends on the robust exchange of
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. Individual will develop and test novel computational models of the neural activity generated by electrical stimulation of the brain. Also, perform data analysis utilizing medical imaging data, computer models
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information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke aspires to create a community built on collaboration
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brain evolution. We employ a multifaceted strategy to bridge developmental neurobiology, RNA biology, and evolution. Learn more about our interests, motivations and discoveries: https://sites.duke.edu
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information, national origin, race, religion, (including pregnancy and pregnancy related conditions), sexual orientation, or military status. Duke aspires to create a community built on collaboration
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, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control the dynamics of microbial communities in time and space. Ongoing projects