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Postdoctoral Fellow Machine Learning Jobs (NOW HIRING)

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Postdoctoral Fellow Machine Learning information

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$25K

$59K

$83.5K

How much do postdoctoral fellow machine learning jobs pay per year?

As of Jun 26, 2026, the average yearly pay for postdoctoral fellow machine learning in the United States is $59,022.00, according to ZipRecruiter salary data. Most workers in this role earn between $49,000.00 and $66,500.00 per year, depending on experience, location, and employer.

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 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.
More about Postdoctoral Fellow Machine Learning jobs
What cities are hiring for Postdoctoral Fellow Machine Learning jobs? Cities with the most Postdoctoral Fellow Machine Learning job openings:
What states have the most Postdoctoral Fellow Machine Learning jobs? States with the most job openings for Postdoctoral Fellow Machine Learning jobs include:
Infographic showing various Postdoctoral Fellow Machine Learning job openings in the United States as of June 2026, with employment types broken down into 77% Full Time, and 23% Part Time. Highlights an 90% Physical, and 10% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.
Postdoctoral Fellow - Biostatistics

Postdoctoral Fellow - Biostatistics

MD Anderson

Houston, TX

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Posted 20 days ago


MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 165 frontline employees who took The Breakroom Quiz

33rd of 876 rated healthcare providers


Job description

The Department of Biostatistics at The University of Texas MD Anderson Cancer Center invites applications for a postdoctoral fellowship in bioinformatics, computational biology, and biostatistics. This position is designed for candidates interested in pursuing methodological research in spatial omics data science while also contributing to collaborative translational projects. The fellow will develop novel statistical and computational methods for integrative spatial omics data analysis, derive and implement related computational models, and create software tools for reproducible analysis. In addition, the fellow will participate in collaborative studies with Dr. Vincent Bernard Pagan's group and other clinical and biological investigators, analyzing spatial omics data generated from clinical trials and related translational research at MD Anderson. The postdoctoral fellow will be supervised by Dr. Ziyi Li and Dr. Vincent Bernard Pagan and will have the opportunity to contribute to high-impact interdisciplinary projects at the interface of computational methodology, cancer biology, and clinical research.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
The postdoctoral trainee will develop advanced expertise in statistical and computational methods for integrative spatial omics data analysis, including high-dimensional modeling, feature extraction, multimodal data integration, and spatially informed machine learning. The training will emphasize the development of novel bioinformatics and computational methodologies, including model derivation, algorithm development, software implementation, and reproducible research practices. The trainee will strengthen programming and computational skills for large-scale biological data analysis, gaining extensive experience in R, Python, C++, and related computational platforms. In addition, the trainee will gain hands-on experience analyzing spatial omics data generated from clinical trials and translational studies, working closely with physicians and biologists to interpret results and generate clinically meaningful insights. Through this process, the trainee will enhance their ability to design, evaluate, and apply innovative statistical and machine learning approaches to complex biomedical problems, while building a strong foundation for independent methodology research, interdisciplinary collaboration, and high-impact scientific publication in biostatistics, bioinformatics, and precision medicine.
ELIGIBILITY REQUIREMENTS
Applicants must have a recent PhD in bioinformatics/biostatistics/computational biology from a reputed University/Institute or within 0-1 years of graduation. At least one first author publication in a peer reviewed journal stemming from PhD studies is required. A solid background in spatial omics, single cell data analysis, and computation is required. Some experience with machine learning and AI is desirable.
Please send CV and information on three referees directly to zli16@mdanderson.org.
POSITION INFORMATION
MD Anderson offers full-time postdoc positions with a salary ranging from $64,000 to $76,000. depending on the number of years of postgraduate experience. The University of Texas MD Anderson Cancer Center offers excellent benefits, including medical, dental, paid time off, retirement, tuition benefits, educational opportunities, and individual and team recognition
Offsite work arrangements are subject to approval and may be modified or revoked at any time based on business needs, performance considerations, or regulatory requirements.
This position may be responsible for maintaining the security and integrity of critical infrastructure, as defined in Section 113.001(2) of the Texas Business and Commerce Code and therefore may require routine reviews and screening. The ability to satisfy and maintain all requirements necessary to ensure the continued security and integrity of such infrastructure is a condition of hire and continued employment.
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, gender, sexual orientation, gender identity/expression, disability, protected veteran status, genetic information, or any other basis protected by institutional policy or by federal, state or local laws unless such distinction is required by law. http://www.mdanderson.org/about-us/legal-and-policy/legal-statements/eeo-affirmative-action.html

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