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Postdoctoral Fellow Machine Learning Jobs in Virginia

Postdoctoral Associate Apply now Back to search results Job no: 536240 Work type: Research Faculty ... Machine Learning (SciML). They will be expected to collaborate with members of the group as well as ...

Neuropsychology Fellow

Roanoke, VA · On-site

$48.10K - $65.30K/yr

A courageous team that is always learning, never discouraged and forever curious. Headquartered in ... The Virginia Tech Carilion School of Medicine Postdoctoral Fellowship in Clinical Neuropsychology ...

Neuropsychology Fellow

Roanoke, VA

$48.10K - $65.30K/yr

A courageous team that is always learning, never discouraged and forever curious. Headquartered in ... The Virginia Tech Carilion School of Medicine Postdoctoral Fellowship in Clinical Neuropsychology ...

The postdoctoral associate will have opportunities to collaborate with experts in AI and machine learning. The post-doctoral associate will also be responsible for data management and dissemination ...

Postdoctoral Associate Apply now Back to search results Job no: 536195 Work type: Research Faculty ... Demonstrated research expertise in federated learning, differential privacy, machine learning for ...

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Postdoctoral Fellow Machine Learning information

What are the key skills and qualifications needed to thrive as a Postdoctoral Fellow in Machine Learning, and why are they important?

To thrive as a Postdoctoral Fellow in Machine Learning, you need a strong background in computer science, mathematics, and statistics, typically supported by a PhD and relevant research experience. Familiarity with programming languages such as Python, machine learning frameworks like TensorFlow or PyTorch, and experience in high-performance computing environments are commonly required. Strong analytical thinking, effective scientific communication, and collaboration skills help you contribute to research teams and disseminate findings. These skills and qualities are crucial for advancing research, developing innovative solutions, and building a successful academic or industry career in machine learning.

What are some common challenges faced by Postdoctoral Fellows in Machine Learning, and how can they be addressed?

Postdoctoral Fellows in Machine Learning often encounter challenges such as balancing independent research with collaborative projects, staying current with rapidly evolving technologies, and securing funding or publishing in top-tier journals. To address these, it's helpful to establish clear communication with mentors and collaborators, set aside dedicated time for reading recent literature, and actively seek feedback on research drafts. Building a professional network through conferences and seminars can also open opportunities for collaboration and career advancement.

What is a Postdoctoral Fellow in Machine Learning?

A Postdoctoral Fellow in Machine Learning is a researcher who has recently completed their PhD and is engaged in advanced research in the field of machine learning. This role typically involves conducting independent or collaborative research, publishing scientific papers, and sometimes mentoring students. Postdoctoral fellows often work at universities, research institutes, or industry labs, focusing on developing new algorithms, improving existing models, or applying machine learning techniques to specific problems. The position is usually temporary, lasting one to three years, and aims to prepare researchers for permanent academic or industry roles.

What is the difference between Postdoctoral Fellow Machine Learning vs Postdoctoral Research Scientist?

AspectPostdoctoral Fellow Machine LearningPostdoctoral Research Scientist
Required credentialsPhD in Computer Science, Data Science, or related fieldPhD in relevant field, often with specialized research experience
Work environmentAcademic labs, universities, research institutionsResearch labs, industry R&D departments, tech companies
Employer and industry usagePrimarily academia, government researchPrimarily industry, corporate research divisions
Common search and comparison intentUnderstanding academic research roles in machine learningExploring industry-focused research career paths

Postdoctoral Fellow Machine Learning roles typically focus on academic research, requiring a PhD and working in universities or research institutions. In contrast, Postdoctoral Research Scientist positions are often industry-based, emphasizing applied research within corporate R&D departments. Both roles involve advanced machine learning expertise but differ mainly in work environment and career trajectory.

What are popular job titles related to Postdoctoral Fellow Machine Learning jobs in Virginia? For Postdoctoral Fellow Machine Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Postdoctoral Fellow Machine Learning jobs in Virginia look for? The top searched job categories for Postdoctoral Fellow Machine Learning jobs in Virginia are:
Postdoctoral Associate (Buczynski Lab)

Postdoctoral Associate (Buczynski Lab)

Virginia Tech

Blacksburg, VA

Other

Posted 22 days ago


Virginia Tech rating

7.7

Company rating: 7.7 out of 10

Based on 64 frontline employees who took The Breakroom Quiz

211th of 530 rated colleges and universities


Job description

Postdoctoral Associate (Buczynski Lab)

Apply now Back to search results 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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