2

Part Time Machine Learning Postdoc Jobs (NOW HIRING)

$152K/yr

We're looking for the person who brings an AI and Machine Learning curriculum to life for students: hosts the live sessions, reviews the work, runs the model and system review boards, and sets the ...

Learning Manager

Atlanta, GA · On-site

$72K - $78K/yr

Full-time, part-time or flexible Eligibility: You must be eligible to work in the USA The role We ... Artificial intelligence and machine learning applications are already changing many aspects of our ...

Learning Manager

Atlanta, GA · On-site +1

$72K - $78K/yr

Full-time, part-time or flexible Eligibility: You must be eligible to work in the USA The role We ... Artificial intelligence and machine learning applications are already changing many aspects of our ...

The internship will provide exposure to remote sensing, spatial analytics, machine learning, and ... Hours: Part-time or full-time depending on availability and academic schedule * Mentorship: Interns ...

Showing results 21-40

Part Time Machine Learning Postdoc information

What is a part time machine learning postdoc?

A Part Time Machine Learning Postdoc is a researcher who holds a postdoctoral position in the field of machine learning, but works fewer hours than a standard full-time appointment. These roles typically involve conducting advanced research, publishing papers, and contributing to academic or industry projects while allowing for flexibility in work hours. Such positions are ideal for those who may have other commitments, such as teaching, consulting, or personal responsibilities, and still want to further their research careers. The expectations and benefits may differ from full-time roles, but they offer valuable experience and networking opportunities in the rapidly evolving field of machine learning.

What are the key skills and qualifications needed to thrive as a part time machine learning postdoc?

To thrive as a Part Time Machine Learning Postdoc, you need a Ph.D. in a relevant field, strong research experience, and deep understanding of machine learning algorithms and statistical methods. Proficiency with programming languages like Python, ML frameworks (e.g., TensorFlow, PyTorch), and experience with data analysis tools is crucial. Excellent problem-solving, effective communication, and time management skills help you balance research demands and collaboration. These skills ensure impactful contributions to research projects, efficient workflow, and successful dissemination of findings.

How does working part-time as a machine learning postdoc typically impact collaboration with research teams and project timelines?

Part-time Machine Learning Postdocs often collaborate closely with both faculty and full-time researchers, which requires clear communication and proactive scheduling to ensure smooth progress on shared projects. Balancing part-time hours may mean prioritizing specific tasks and being especially organized to keep projects on track. Many teams accommodate flexible work arrangements, but it's crucial to set expectations around availability and deliverables. Regular check-ins and use of collaborative tools can help maintain strong connections with the team and ensure that research milestones are met.

What is the difference between Part Time Machine Learning Postdoc vs Part Time Data Scientist?

AspectPart Time Machine Learning PostdocPart Time Data Scientist
Required CredentialsPhD in Machine Learning, Computer Science, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; often prefers experience
Work EnvironmentAcademic research settings, universities, research labsIndustry companies, startups, corporate analytics teams
Employer & Industry UsageUniversities, research institutions, government agenciesTech firms, finance, healthcare, retail sectors
Common Search & Comparison IntentUnderstanding academic research roles in machine learningApplying machine learning techniques in industry projects

While both roles involve machine learning expertise, a Part Time Machine Learning Postdoc typically focuses on academic research, publishing papers, and advancing theoretical knowledge. In contrast, a Part Time Data Scientist applies machine learning models to solve practical industry problems, often working directly with business data and stakeholders.

More about Part Time Machine Learning Postdoc jobs

What cities are hiring for Part Time Machine Learning Postdoc jobs?

Cities with the most Part Time Machine Learning Postdoc job openings:

What are the most commonly searched types of Machine Learning Postdoc jobs?

The most popular types of Machine Learning Postdoc jobs are:

What states have the most Part Time Machine Learning Postdoc jobs?

States with the most job openings for Part Time Machine Learning Postdoc jobs include:

What are popular job titles related to Part Time Machine Learning Postdoc jobs?

For Part Time Machine Learning Postdoc jobs, the most frequently searched job titles are:

What other helpful pages are available for Part Time Machine Learning Postdoc?

Other pages related to Part Time Machine Learning Postdoc:

Infographic showing various Part Time Machine Learning Postdoc job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 74% Full Time, 22% Part Time, and 2% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Post-Doctoral Fellow in Acoustics AF7771

Stillwater, OK • On-site

$46K - $62K/yr

Full-time, Part-time

Posted 13 days ago


Job description

Post-Doctoral Fellow - Acoustics

Position Summary:

The School of Mechanical and Aerospace Engineering at Oklahoma State University invites applications for a Post-Doctoral Fellow position to join Prof. Yangfan Liu’s research group. The successful candidate will lead interdisciplinary research at the intersection of machine learning, physical and computational acoustics, vibration/noise control, and signal processing.

This is a postdoctoral appointment intended for candidates seeking to contribute and manage ongoing sponsored research projects and supervise students. Ideal candidates will have a strong cross-disciplinary background in acoustics, audio/speech or acoustic signal processing, computational acoustics, and modern machine learning. Key responsibilities include conducting independent research, developing AI-acoustics methodologies with strong practical and translational value, managing projects, contributing to proposals and sponsor reports, preparing high-quality peer-reviewed publications, and mentoring graduate and undergraduate students. The position offers opportunities to collaborate with academic researchers, industrial partners, and students across multiple disciplines.

Responsibilities and Duties

50% – Research

  • Develop machine learning, deep learning, and physics-aware models for acoustics, vibration, NVH, audio/speech, and signal-processing applications.
  • Build surrogate models, inverse-design frameworks, and data-driven optimization methods for acoustic materials, computational acoustics, compressor/vehicle-cabin acoustics, or related vibroacoustic systems.
  • Design and perform experiments to validate developed methodologies and build proof-of-concept demos.
  • Prepare peer-reviewed publications in leading journals and conferences in acoustics, signal processing, machine learning, mechanical engineering, and related fields.
  • Develop reproducible code, analysis pipelines, and technical documentation to support research dissemination and technology transfer.

20% – Research Management

  • Manage day-to-day project tasks to meet sponsor requirements, research milestones, and publication timelines.
  • Coordinate research activities among faculty, students, and external collaborators; organize technical meetings and track deliverables.
  • Provide technical leadership on model development, data analysis, simulation/measurement workflows, and manuscript preparation.

20% – Proposal Development & Professional Service

  • Contribute to research proposals for federal agencies, industry sponsors, and university-industry collaborative programs.
  • Assist with grant management, sponsor communications, technical reporting, and partnership development.
  • Represent the research group at conferences, workshops, sponsor meetings, and professional events.
  • Participate in peer review, professional societies, and academic service activities as appropriate.

10% – Student Mentoring

  • Mentor graduate and undergraduate students in machine learning, acoustics, signal processing, simulation, and data analysis.
  • Guide students in research planning, coding practices, experiment/simulation design, data interpretation, technical writing, and presentations.

Qualifications:

  • Ph.D. in Mechanical or Aerospace Engineering, Acoustics, Electrical and Computer Engineering, Computer Science, Applied Physics, or a closely related field.
  • Strong background in acoustics, vibration/NVH, audio/speech processing, acoustic signal processing, computational acoustics, or related areas.
  • Demonstrated expertise in machine learning or AI methods, such as deep learning, physics-informed or physics-aware learning, surrogate modeling, inverse design, generative models, optimization, or uncertainty-aware modeling.
  • Strong record of scholarly research, including first-author or major-contribution publications in high-quality peer-reviewed journals and conferences.
  • Experience with numerical simulation, experimental/measurement data, acoustic materials/structures, vehicle-cabin acoustics, HAV equipment noise, aerodynamic noise, or speech/audio systems is preferred.
  • Proficiency in research programming and data analysis, preferably using Python and ML frameworks such as PyTorch or TensorFlow; experience with MATLAB, COMSOL, FEM/CFD/acoustic tools, or signal-processing toolchains is a plus.
  • Demonstrated ability to communicate complex technical concepts clearly in reports, manuscripts, presentations, and interdisciplinary team settings.
  • Experience mentoring students, coordinating research tasks, leading collaborative projects, or managing team deliverables is strongly preferred.

SALARY AND BENEFITS:

Salary will be commensurate with education, experience, qualifications, appointment percentage, and contingent on available funding. Benefits, if applicable, will follow Oklahoma State University policies for part-time postdoctoral appointments. Applicants should refer to Oklahoma State University Human Resources for current benefits information.

SPECIAL INSTRUCTIONS TO APPLICANTS:

The process of reviewing applications will begin soon and will continue until a successful candidate is selected.

Interested and qualified candidates should submit a single PDF file including:

  • a cover letter addressing research interests, relevant experience, and skills that fulfill the requirements;
  • a Curriculum Vitae;
  • up to three representative publications.

Questions about the position should be directed to Prof. Yangfan Liu at yangfan.liu@okstate.edu.