Hiring.Camp

Postdoctoral Fellow of Computer Science (Robust and Adaptive Intelligence)

Williammary

·

2 days ago

Salary
$70k – $75k
Location
Integrated Science Center, United States of America
Workplace
Remote
Type
Full-time
Education
PhD
Source
Workday

Description

Job Requisition:

JR101939 Postdoctoral Fellow of Computer Science (Robust and Adaptive Intelligence) (Open)

Job Posting Title:

Postdoctoral Fellow of Computer Science (Robust and Adaptive Intelligence)

Department:

CC00100 WM001 | CDSP | Computer Science Dept

Job Family:

Faculty - Postdoctoral Research Associate (12 months)

Worker Sub-Type:

Fixed Term (requires end date - benefited) (Fixed Term)

Job Requisition Primary Location:

Integrated Science Center

Primary Job Posting Location:

Posting Location - William & Mary

Job Description Summary:

The Department of Computer Science at William & Mary, a public university of the Commonwealth of Virginia, invites applications for a postdoctoral fellow position in the Robust and Adaptive Intelligence Laboratory, expected to begin September 10, 2026. We seek an individual to contribute to two intertwined threads of research: (i) AI-based approaches for routing under uncertainty, including dynamic dispatch and stochastic and online vehicle routing at scale; and (ii) explainability of AI-based sequential decision-making, combining formal logic, large language models, and planning so that decisions produced by complex systems can be understood, audited, and trusted. The appointed researcher will publish in top venues in artificial intelligence, machine learning, and operations research; contribute to open-source releases; and work closely with graduate students in the lab.

The initial appointment is for one year, with the possibility of renewal based on performance and funding availability.

Job Description:

Required Qualifications:

  • Applicant must hold a Ph.D. in Computer Science, or a related field, at the time appointment begins (expected to begin September 10, 2026).

Preferred Qualifications:

  • Experience in Sequential Decision-Making under Uncertainty, Planning, and Machine Learning.
  • Strong software-engineering skills and a track record of publishing at top AI/ML/OR venues or of deploying research systems in real-world settings are welcome.

Applicants must apply online at https://www.wm.edu/offices/uhr/careers/. Submit a curriculum vitae, a cover letter, two names for letters of references, and a statement of research. For full consideration, submit application materials by the review begin date: August 10, 2026. Applications received after the review date will be considered if needed.


Information on the Department of Computer Science may be found at https://cdsp.wm.edu/computerscience/.


Posting Range: $70,000 - $75,000, commensurate with experience and internal alignment

Additional Job Description:

Job Profile:

JP0518 - Postdoctoral Research Associate (12 months) - Exempt - Salary - S99

Qualifications:

Compensation Grade:

S99

Recruiting Start Date:

2026-07-21

Position Restrictions:

Restricted Funds & Restricted Term

EEO is the Law.  Applicants can learn more about William & Mary’s status as an equal opportunity employer by viewing the "Know Your Rights" poster published by the U.S. Equal Employment Opportunity Commission. https://www.eeoc.gov/know-your-rights-workplace-discrimination-illegal

 

Background Check: William & Mary is committed to providing a safe campus community. W&M conducts background investigations for applicants being considered for employment. Background investigations include reference checks, a criminal history record check, and when appropriate, a financial (credit) report or driving history check. 

 

Remote Work Disclaimer: Remote work eligibility is not guaranteed and is subject to approval. Employee eligibility depends on the likelihood of the employee succeeding in a remote work arrangement and the supervisor’s ability to manage remote workers. Departments and/or Human Resources may amend, alter, change, delete, or modify eligibility. 

Skills

Machine Learning

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