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Machine Learning Postdoc Jobs in Dallas, TX (NOW HIRING)

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Machine Learning Postdoc information

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$21.7K

$112.5K

$211.7K

How much do machine learning postdoc jobs pay per year?

As of Aug 17, 2026, the average yearly pay for machine learning postdoc in Dallas, TX is $112,490.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,233.00 and $154,924.00 per year, depending on experience, location, and employer.

What is a machine learning postdoc?

A Machine Learning Postdoc is a research-focused position typically held after earning a Ph.D. in a related field. It involves conducting advanced research in machine learning, developing new algorithms, and publishing in top-tier conferences and journals. Postdocs often collaborate with faculty, industry partners, and other researchers to advance the state of the art in AI. The role may include mentoring students and contributing to grant proposals. It serves as a bridge between doctoral studies and a long-term academic or industry research career.

What are the typical responsibilities and collaborative aspects of a machine learning postdoc?

A Machine Learning Postdoc typically conducts original research, develops and tests new algorithms, and contributes to academic publications or patent applications. Daily tasks often involve data analysis, model building, and experimentation using advanced computational tools. Collaboration is key in this role, as postdocs frequently work alongside faculty, graduate students, and external industry partners to advance research objectives. Additionally, they may mentor junior researchers or students, present at conferences, and participate in grant writing or project planning. This mix of independent research and team collaboration fosters both professional growth and impactful scientific advancements.

What are the key skills and qualifications needed to thrive in a machine learning postdoc position?

To thrive as a Machine Learning Postdoc, you need a deep understanding of machine learning algorithms, statistical modeling, and research methodology, typically supported by a completed PhD in a related field. Proficiency with programming languages like Python or R, experience with ML libraries (e.g., TensorFlow or PyTorch), and familiarity with large-scale datasets and cloud computing platforms are important. Strong analytical thinking, effective communication, and the ability to collaborate across multidisciplinary teams are standout soft skills in this position. These qualifications ensure innovative research contributions, successful project execution, and effective dissemination of findings in both academic and applied settings.

What are the most commonly searched types of Machine Learning Postdoc jobs in Dallas, TX?

The most popular types of Machine Learning Postdoc jobs in Dallas, TX are:

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For Machine Learning Postdoc jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Machine Learning Postdoc jobs in Dallas, TX look for?

The top searched job categories for Machine Learning Postdoc jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Machine Learning Postdoc jobs?

Cities near Dallas, TX with the most Machine Learning Postdoc job openings:

Machine Learning Postdoc jobs near you
Infographic showing various Machine Learning Postdoc job openings in Dallas, TX as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 96% In-person, and 4% Hybrid job distribution, with an average salary of $112,490 per year, or $54.1 per hour.

POSTDOCTORAL RESEARCHER-HDSB-Xiao Lab-[Req#: 914775, Position#: 123269]

UT Southwestern Medical Center

Dallas, TX • On-site

Full-time

Re-posted 28 days ago

UT Southwestern rating

7.9

Company rating: 7.9 out of 10

Based on 152 frontline employees who took The Breakroom Quiz

107th of 887 rated healthcare providers

Job description

Description
A postdoctoral fellow position in AI and Data Science is now available in the laboratories of Dr. Guanghua Xiao at the Quantitative Biomedical Research Center in the Peter O'Donnell School of Public Health at UT Southwestern Medical Center at UT Southwestern Medical Center at Dallas.
The Quantitative Biomedical Research Center (QBRC) is a well-established interdisciplinary research center at UT Southwestern that brings together experts in artificial intelligence, machine learning, predictive modeling, clinical informatics, digital pathology, and biomedical data science. Our goal is to develop cutting-edge computational methods and tools that enable novel discoveries and support data-driven decision-making in health care and public health.
We are seeking highly motivated, creative, and collaborative postdoctoral candidates to join our dynamic team and contribute to a portfolio of research projects applying AI and data science to real-world health care and public health data. The main research areas include:
• Electronic Health Records (EHR)
• Medical imaging (e.g., radiology and pathology)
• Real-time monitoring and wearable sensor data
The successful candidate will have the opportunity to lead and contribute to innovative projects in clinical prediction modeling, disease progression modeling, population health surveillance, and digital biomarker discovery. Our center also supports strong collaborations with clinicians, data scientists, and public health researchers.
Qualifications:
• Ph.D. in Computer Science, Statistics, Biomedical Informatics, Engineering, or a related field.
• Strong programming skills and experience with machine learning, deep learning, or AI applications.
• Interest or experience in working with large-scale health-related datasets.
Qualifications
Qualifications:
• Ph.D. in Computer Science, Statistics, Biomedical Informatics, Engineering, or a related field.
• Strong programming skills and experience with machine learning, deep learning, or AI applications.
• Interest or experience in working with large-scale health-related datasets.
Application Instructions
Application materials must be submitted through Interfolio.
Interested individuals must upload a CV, cover letter, and a list of three references.

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