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Postdoctoral Fellow Machine Learning Jobs (NOW HIRING)

Morehouse School of Medicine

Postdoctoral Fellow

Atlanta, GA · On-site

$47K - $63K/yr

Posting Number ACAD532 Job Title Postdoctoral Fellow Position Title Postdoctoral Fellow Position ... Perl); machine learning/AI applications. • Experience with CRISPR techniques; microbiome ...

Basis Research

Postdoctoral Fellow, Open Call

Cambridge, MA · On-site

$53K - $72K/yr

Postdoctoral Fellowships at Basis Postdoctoral Fellowships at Basis offer an immersive research ... Researchers holding a PhD in computer science, artificial intelligence, machine learning ...

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

As of Aug 6, 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?

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.
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What cities are hiring for Postdoctoral Fellow Machine Learning jobs? Cities with the most Postdoctoral Fellow Machine Learning job openings:
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What job categories do people searching Postdoctoral Fellow Machine Learning jobs look for? The top searched job categories for Postdoctoral Fellow Machine Learning jobs are:
Postdoctoral Fellow Machine Learning jobs near you
Infographic showing various Postdoctoral Fellow Machine Learning job openings in the United States as of July 2026, with employment types broken down into 78% Full Time, 21% Part Time, and 1% Contract. Highlights an 89% Physical, and 11% Remote job distribution, with an average salary of $59,022 per year, or $28.4 per hour.

Postdoctoral Fellow - Biostatistics

MD Anderson Cancer Center

Houston, TX • On-site

$64K - $76K/yr

Full-time

Medical, Dental, Retirement, PTO

Re-posted 7 hours ago

MD Anderson Cancer Center rating

8.4

Company rating: 8.4 out of 10

Based on 170 frontline employees who took The Breakroom Quiz

25th of 887 rated healthcare providers

Job description

The Department of Biostatistics at has one postdoctoral fellow position open for biostatistics and data science methodology research in clinical trials. The main focus is research and publication. The primary focus will be to develop novel methods for causal AI/inference methods, adaptive Bayesian clinical trial designs, derive related statistical theory, produce software for implementation, incorporate biomarkers in clinical trial design and analysis, and build statistical learning tools for large data sets. The postdoctoral fellow will work under the supervision of Dr. Liang on challenging and important clinical and biological projects that involve complex statistical modeling, data analysis, and computation.
All duties and responsibilities are carried out in compliance with institutional policies, ethical research standards, and applicable federal and state regulations.
LEARNING OBJECTIVES
Trainee will learn through various research projects, with a primary focus on: (1) developing novel statistical and data science methods, as well as user-friendly software, for integrating AI tools to evaluate novel treatments or design future clinical trials in overall population or subgroups, and (2) analyzing real-world and institutional medical datasets. A major methodological focus will be integrating machine learning/artificial intelligence tools, causal inference methods, Bayesian techniques, and adaptive designs to build innovative, next-generation tools for adaptively and efficiently evaluating treatment effectiveness and learning optimal treatment decisions that may vary by different patients' subgroups.
ELIGIBILITY REQUIREMENTS
Applicants must have a recent PhD in biostatistics or statistics 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. Candidates must have strong methodological training in statistics or biostatistics, especially in causal inference or semiparametric methods, and have strong computer programming skills, in particular using R or Python. Expertise or skills in the following areas are highly desirable: Causal inference, double/debias machine learning, semiparametric methods, Bayesian MCMC computational methods, adaptive clinical trials, and machine learning for estimation or decision-making.
Please send CV and information on three referees directly to mliang2@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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