Postdoctoral Fellow in Computer Science - From Theory to Practice: Reinforcement Learning for Large Scale Foundation Model Post-Training

Successful candidates must be able to work as part of a team performing research to support the project. Additional responsibilities include preparing publications and reports, mentoring graduate students, and collaborating with other groups. Successful candidates will be self-motivated, have a strong work ethic, be technically skilled, and have strong oral and written skills. Start dates are flexible, but the positions will be filled as soon as possible and will initially be for one year, renewable for additional years following performance review and funding availability. Basic Qualifications Ph.D. in computer science, applied physics, applied math, or a related discipline. Additional Qualifications Individuals with a demonstrated track record in scientific research, which can be evidenced through publications, technical reports, or impactful software projects. Special Instructions A complete application must include a curriculum vitae, 2-5 letters of reference, 2 publication samples, and a 1-2page research statement. In your cover letter, please also address the following supplemental questions: (1) What sorts of projects would you like to carry out in the lab? (2) What are your greatest strengths, and what areas would you like to improve?

Pay offered to the selected candidate is dependent on factors such as rank, years of experience, training or qualification, field of scholarship, and accomplishments in the field. Minimum Number of References Required 2 Maximum Number of References Allowed 3 Keywords Machine Learning Reinforcement LearningFoundational Models Post-Training Large Language Models EEO/Non-Discrimination Commitment Statement

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Supplemental Questions

Required fields are indicated with an asterisk (*). Applicant DocumentsRequired Documents

1. Curriculum Vitae
2. Cover Letter
3. Statement of Research
4. Publication
5. Publication 2
6. List of References