Postdoctoral Research Position: Safe and Explainable Multiagent Reinforcement Learning, Compute... at InsideHigherEd

External Applicants: Please ensure all required documents are ready to upload before beginning your application, including your resume, cover letter, and any additional materials specified in the job description.Cover Letter and Supporting Documents:Navigate to the "My Experience" application page.Locate the "Resume/CV" document upload section at the bottom of the page.Use the "Select Files" button to upload your cover letter, resume, and any other required supporting documents. You can select multiple files.Important Note: The "My Experience" page is the only opportunity to attach your cover letter, resume, and supporting documents. You will not be able to modify your application or add attachments after submission.Current Employees:Apply from your existing Workday account in the Jobs Hub. Do not apply from this website. A cover letter is required for all positions; optional for facilities, campus services, and hospitality roles unless otherwise specified.Job Description SummaryWe invite applications for a 2-year postdoctoral research position in Safe and Explainable Multiagent Reinforcement Learning (MARL). This position is supported by an NSF-funded project focused on developing foundational methods for ensuring the safety and interpretability of MARL systems.Application InstructionsTo apply, please submit:1.A cover letter describing your background and research interests2.Curriculum vitae (CV)3.Two representative publications4.Contact information for 2–3 referencesApplications will be reviewed on a rolling basis until the position is filled.Job DescriptionEssential Functions:You will work with the PI and collaborators in advancing theoretically sound and practically applicable MARL algorithms, with an emphasis on safety, explainability, and robust real-world deployment. The postdoctoral researcher will contribute to one or more of the following areas:Safe Learning in MARLLearning robust policies under uncertainty with built-in safety mechanismsPolicy Explainability and TestingDeveloping tools and methods to visualize, explain, and verify MARL policiesRobustness and Fault ToleranceDesigning MARL algorithms resilient to adversarial conditions or partial failuresRequired Education, Knowledge, Skills, Abilities:Ph.D. in Computer Science, Electrical Engineering, or a related fieldStrong background in reinforcement learning (preferably MARL)Proficiency with machine learning tools (e.g., PyTorch, RL libraries)Strong publication record in relevant venues (e.g., NeurIPS, ICLR, AAAI, AAMAS)Strong communication and collaboration skillsPreferred Education, Knowledge, Skills, Abilities:Experience in formal verification, interpretability, or AI safetyInterest in interdisciplinary research and real-world impactAccountabilities:Responsible for own work.Physical Requirements:Sedentary work primarily involves sitting/standing; communicating with others to exchange information; repeating motions that may include the wrists, hands, and/or fingers; and assessing the accuracy, neatness, and thoroughness of the work assigned.Environmental Conditions:No environmental conditions.Additional Job DescriptionTime Type RequirementFull timeNote to Applicant:This position profile identifies the key responsibilities and expectations for performance. It cannot encompass all specific job tasks that an employee may be required to perform. Employees are required to follow any other job-related instructions and perform job-related duties as may be reasonably assigned by his/her supervisor.In order to provide a safe and productive learning and living community, Wake Forest University conducts background investigations and drug screens for all final staff candidates being considered for employment.Equal Opportunity StatementThe University is an equal opportunity employer and welcomes all qualified candidates to apply without regard to race, color, religion, national origin, sex, age, sexual orientation, gender identity and expression, genetic information, disability and military or veteran status. Accommodations for ApplicantsIf you are an individual with a disability and need an accommodation to participate in the application or interview process, please contact [email protected] or (336) 758-4700.