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Biomedical Machine Learning Jobs in Texas (NOW HIRING)

Rice University

Research Scientist

Houston, TX · On-site

$80K/yr

Research experience in computational imaging, biomedical imaging, biophotonics, microscopy, optical system design, machine learning, or related areas. * Experience designing, developing, fabricating ...

Rice University

Research Scientist

Houston, TX · On-site

$80K/yr

Research experience in computational imaging, biomedical imaging, biophotonics, microscopy, optical system design, machine learning, or related areas. * Experience designing, developing, fabricating ...

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How much do biomedical machine learning jobs pay per hour?

As of Jun 29, 2026, the average hourly pay for biomedical machine learning in Texas is $26.58, according to ZipRecruiter salary data. Most workers in this role earn between $22.60 and $30.00 per hour, depending on experience, location, and employer.

What is a Biomedical Machine Learning job?

A Biomedical Machine Learning job involves developing and applying machine learning algorithms to analyze biomedical data for healthcare and research applications. Professionals in this field work with medical imaging, genomics, electronic health records, and wearable device data to improve disease diagnosis, treatment, and patient outcomes. They collaborate with researchers, clinicians, and data scientists to design predictive models and extract insights from complex biological data. This role requires expertise in machine learning, data processing, and domain-specific knowledge in healthcare or life sciences.

What does a typical day look like for someone in a Biomedical Machine Learning role?

A typical day in Biomedical Machine Learning involves cleaning and preparing biomedical datasets, developing or refining machine learning models, running experiments, and interpreting results in collaboration with domain experts such as bioinformaticians and clinicians. Professionals often participate in team meetings to discuss project goals, share insights, and adjust research directions based on feedback. The role may also involve reading scientific literature to stay current with new methodologies and contributing to academic publications or technical documentation. Working closely with both technical and healthcare-focused colleagues, you'll help translate data-driven insights into meaningful biomedical solutions that impact patient care or research outcomes.

What are the key skills and qualifications needed to thrive in the Biomedical Machine Learning position, and why are they important?

To thrive in Biomedical Machine Learning, you need a solid background in statistics, machine learning, programming (Python or R), and a strong understanding of biological or medical data, often supported by advanced degrees in computer science, biomedical engineering, or related fields. Experience with frameworks like TensorFlow, PyTorch, and familiarity with biomedical datasets is highly valued, and certifications in data science or biomedical informatics can be advantageous. Strong analytical thinking, communication skills, and the ability to collaborate with interdisciplinary teams are crucial soft skills. These competencies are vital to developing robust models that address complex healthcare challenges while ensuring scientific rigor and regulatory compliance.

What are the most commonly searched types of Biomedical Machine Learning jobs in Texas? The most popular types of Biomedical Machine Learning jobs in Texas are:
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What cities in Texas are hiring for Biomedical Machine Learning jobs? Cities in Texas with the most Biomedical Machine Learning job openings:
Biomedical Machine Learning jobs near you
Infographic showing various Biomedical Machine Learning job openings in Texas as of June 2026, with employment types broken down into 2% As Needed, 7% Full Time, 85% Part Time, 2% Temporary, and 4% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $55,278 per year, or $26.6 per hour.
POSTDOCTORAL RESEARCHER-HDSB-Xiao Lab-[Req#: 914775, Position#: 123269]

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

UT Southwestern Medical Center

Dallas, TX • On-site

Full-time

Posted 9 days ago

UT Southwestern rating

7.8

Company rating: 7.8 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

105th of 877 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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