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Postdoctoral In Reinforcement Learning Jobs in Virginia

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Supervised, unsupervised, and reinforcement learning * Neural networks, decision trees, ensemble ... Bachelor's degree in Computer Science, Engineering, Applied Mathematics, or a related field * 7+ ...

In this role, you will work with cutting-edge technologies to design, develop, and deploy machine ... Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ...

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Supervised, unsupervised, and reinforcement learning * Neural networks, decision trees, ensemble ... Bachelor's degree in Computer Science, Engineering, Applied Mathematics, or a related field * 7+ ...

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Supervised, unsupervised, and reinforcement learning * Neural networks, decision trees, ensemble ... Bachelor's degree in Computer Science, Engineering, Applied Mathematics, or a related field * 7+ ...

In this role, you will work with cutting-edge technologies to design, develop, and deploy machine ... Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ...

In this role, you will work with cutting-edge technologies to design, develop, and deploy machine ... Strong understanding of machine learning algorithms (supervised, unsupervised, reinforcement ...

Showing results 41-60

Postdoctoral In Reinforcement Learning information

What is the difference between Postdoctoral In Reinforcement Learning vs Postdoctoral In Machine Learning?

AspectPostdoctoral In Reinforcement LearningPostdoctoral In Machine Learning
Required CredentialsPhD in Computer Science, AI, or related field; strong programming skills; research experience in reinforcement learningPhD in Computer Science, AI, or related field; strong programming skills; research experience in machine learning
Work EnvironmentAcademic labs, research institutions, industry R&D teams focused on reinforcement learning applicationsAcademic labs, research institutions, industry R&D teams working on various machine learning techniques
Industry UsagePrimarily in AI research, robotics, gaming, and autonomous systemsBroader applications including data analysis, predictive modeling, and AI research

Postdoctoral In Reinforcement Learning specializes in research related to decision-making algorithms and autonomous systems, whereas Postdoctoral In Machine Learning covers a wider range of AI techniques. Both roles require similar credentials but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as a postdoctoral researcher in reinforcement learning?

To thrive as a Postdoctoral Researcher in Reinforcement Learning, you need a PhD in computer science or a related field, with deep expertise in machine learning, statistics, and algorithm development. Proficiency in programming languages such as Python, experience with deep learning frameworks (e.g., TensorFlow or PyTorch), and familiarity with reinforcement learning libraries are typically required. Strong analytical thinking, problem-solving ability, collaboration, and scientific communication skills help you excel in research teams and publish impactful work. These competencies are vital to advancing state-of-the-art research, developing novel algorithms, and contributing to the academic and industrial progress in AI.

What are some common challenges faced by postdoctoral researchers in reinforcement learning, and how can they be addressed?

Postdoctoral researchers in reinforcement learning often face challenges such as balancing independent research projects with collaborative work, staying up-to-date with rapidly evolving literature, and managing the pressure to publish in top conferences. Effective time management, regular engagement with the research community through seminars and workshops, and seeking mentorship from senior colleagues can help address these challenges. Additionally, collaborating with interdisciplinary teams can offer fresh perspectives and support, making it easier to navigate complex research problems.

What is a postdoctoral researcher in reinforcement learning?

A Postdoctoral Researcher in Reinforcement Learning is an individual who has completed a PhD and conducts advanced research in the field of reinforcement learning, a branch of artificial intelligence focused on how agents take actions in environments to maximize rewards. These researchers often work in academic, industrial, or governmental research settings, collaborating on projects that advance the theoretical foundations or practical applications of reinforcement learning. Their responsibilities may include designing experiments, developing algorithms, publishing papers, and mentoring graduate students.
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What job categories do people searching Postdoctoral In Reinforcement Learning jobs in Virginia look for? The top searched job categories for Postdoctoral In Reinforcement Learning jobs in Virginia are:
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Postdoctoral Associate (Buczynski Lab)

Virginia Polytechnic Institute and State University

Blacksburg, VA • On-site

Full-time

Re-posted 2 days ago


Virginia Tech rating

7.8

Company rating: 7.8 out of 10

Based on 65 frontline employees who took The Breakroom Quiz

229th of 617 rated colleges and universities


Job description

Postdoctoral Associate (Buczynski Lab)
Job no: 535789
Work type: Research Faculty
Senior management: College of Science
Department: School of Neuroscience
Location: Blacksburg, Virginia
Categories: Research / Scientific
Job Description
The Gregus/Buczynski Lab is seeking a motivated postdoctoral fellow to join an extramurally funded, highly collaborative research program focused on developing new, mechanistically driven treatments for chronic pain and alcohol use disorder - two major unmet clinical needs.
Our lab integrates behavioral pharmacology, molecular pharmacology, cell based assays, and mass spectrometry to address clinically relevant questions with clear translational potential. Postdoctoral fellows receive hands on training across experimental design, data analysis, and advanced methodologies, while building a professional profile positioned for success in academia, biotech, or pharmaceutical science.
Why work in the Gregus/Buczynski Lab?
work in the Gregus/Buczynski Lab?
• Strong funding & project ownership: Work on funded projects with room to develop
independent ideas and lead publications
• Career focused mentorship: Structured mentorship tailored to your goals (academic
faculty, industry scientist, or hybrid paths)
• Translational science with real-world impact: Research directly relevant to therapeutic
development
• Outstanding collaborative network: Active collaborations with Scripps Research, Mayo
Clinic, University of Texas at Austin, University of Texas at Dallas, industry partners, and
multiple Virginia Tech departments
What you'll do:
• Design, execute, and analyze in vivo and in vitro experiments
• Interpret data and present findings in lab meetings, conferences, and publications
• Collaborate at the interface of academia and industry
• Contribute to grant proposals and co author manuscripts
• Mentor junior trainees as desired, building leadership experience
Mentorship & lab culture
Our lab is team oriented, supportive, collegial, and inclusive. We believe strong science comes from people who feel respected, supported, and excited about their work. Regular availability for individual meetings, collaborative troubleshooting, and transparent expectations are core to our mentoring style. Training plans are customized to your career goals and evolve as you grow.
Required Qualifications
• Ph.D. in neuroscience, pharmacology, or a closely related discipline
• Experience with mammalian cell culture and/or rodent behavioral models
• Strong organizational skills and the ability to manage experiments independently
• Effective written and verbal communication skills
Preferred Qualifications
• Research background in pain, addiction, or related neuropsychiatric disorders
• Experience with mass spectrometry or bioanalytical techniques
• Experience with mouse surgical procedures or in vivo pharmacology
• Prior experience contributing to manuscripts, abstracts, or conference presentations
• Experience in all techniques used in the lab is not required as we value strong scientific
thinking, work ethic, and enthusiasm for learning as much as prior technical expertise.
Note: Candidates with complementary training backgrounds are encouraged to apply if they bring strong motivation to learn new techniques.
Overtime Status
Exempt: Not eligible for overtime
Appointment Type
Restricted
Salary Information
Commensurate with experience
Hours per week
40 hours - exempt position
Review Date
3/31/2026
Additional Information
The successful candidate will be required to have a criminal conviction check.
About Virginia Tech
Dedicated to its motto, Ut Prosim (That I May Serve), Virginia Tech pushes the boundaries of knowledge by taking a hands-on, transdisciplinary approach to preparing scholars to be leaders and problem-solvers. A comprehensive land-grant institution that enhances the quality of life in Virginia and throughout the world, Virginia Tech is an inclusive community dedicated to knowledge, discovery, and creativity. The university offers more than 280 majors to a diverse enrollment of more than 36,000 undergraduate, graduate, and professional students in eight undergraduate colleges, a school of medicine, a veterinary medicine college, Graduate School, and Honors College. The university has a significant presence across Virginia, including Blacksburg, the greater Washington, D.C. area, the Health Sciences and Technology Campus in Roanoke, sites in Newport News and Richmond, and numerous Extension offices and research institutes. A leading global research institution, Virginia Tech conducts more than $650 million in research annually.
Virginia Tech endorses and encourages participation in professional development opportunities and university shared governance. These valuable contributions to university shared governance provide important representation and perspective, along with opportunities for unique and impactful professional development.
Virginia Tech does not discriminate against employees, students, or applicants on the basis of age, color, disability, sex (including pregnancy), gender, gender identity, gender expression, genetic information, ethnicity or national origin, political affiliation, race, religion, sexual orientation, or military status, or otherwise discriminate against employees or applicants who inquire about, discuss, or disclose their compensation or the compensation of other employees or applicants, or on any other basis protected by law.
If you are an individual with a disability and desire an accommodation, please contact Natalie Langowsky at nlangow3@vt.edu during regular business hours at least 10 business days prior to the event.
Advertised: March 11, 2026
Applications close:
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About Virginia Tech

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Virginia Tech, guided by its motto "Ut Prosim" (That I May Serve), embraces a hands-on, interdisciplinary approach to educate scholars as leaders and problem-solvers. As a comprehensive land-grant institution, it enriches the quality of life in Virginia and worldwide, fostering an inclusive community focused on knowledge, discovery, and creativity. With over 280 majors, the university serves a diverse student body of more than 36,000 across undergraduate, graduate, and professional programs. Virginia Tech's presence extends throughout Virginia, including campuses in Northern Virginia, Roanoke, Newport News, and Richmond, along with multiple Extension offices and research centers. As a prominent global research institution, it conducts over $500 million in research annually.

Industry

Colleges, universities, and professional schools

Company size

5,001 - 10,000 Employees

Headquarters location

Blacksburg, VA, US

Year founded

1872

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