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sits at the intersection of AI Safety and Data-Centric AI. We aim to make large-scale ML more reliable, transparent, and aligned with human values. We are specifically interested in: Data-centric AI
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to take up the role you are applying for, please use the information provided on our website to self-assess whether the University is likely to be able to support a visa application - http
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field data, experimental data, and field experiments. Salary is $70,500 annually, plus benefits. Expected start date is mid-June 2027. For examples of our research, see our faculty pages: https
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https://www.ubjobs.buffalo.edu/postings/64251 Employer State Faculty Appointment Term Term Position Type UUP Faculty Bargaining Unit 08 - UUP Posting Detail Information Fiscal Year 2026-2027 Position
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19 Sep 2026 Job Information Organisation/Company CNRS Department Institut de physique et chimie des matériaux de Strasbourg Research Field Engineering Computer science Mathematics Researcher Profile
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5 Sep 2026 Job Information Organisation/Company CNRS Department Laboratoire de Chimie Moléculaire Research Field Chemistry Physics Researcher Profile First Stage Researcher (R1) Application Deadline
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of Openings: 1 Work Location: On Campus The successful applicant will be required to provide the following current verification(s) where 'Yes' is indicated below. Further information is available at: https
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below. Further information is available at: https://careers.usask.ca/tips-for-applying.php Criminal Record Check: Yes Driver's License and Abstract Check: Not Applicable Education/Credential Verification
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 17 hours ago
Information: https://hr.charlotte.edu/benefits/benefits-overview/ Posting Open Date 09/18/2026 Posting Close Date 10/01/2026 Open Until Filled No Proposed Hire Date 10/15/2026 If time-limited please indicate
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About us The Laboratory of Causal Systems Immunology combines large-scale in vivo perturbation experiments, advanced mouse models of immune diseases and causal learning to uncover how genes shape