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Bioinformatics Machine Learning information

What is a Bioinformatics Machine Learning job?

A Bioinformatics Machine Learning job involves applying machine learning techniques to analyze and interpret biological data, such as genomics, proteomics, and medical records. Professionals in this field develop algorithms, build predictive models, and enhance data-driven research in areas like personalized medicine and drug discovery. They work with large datasets, applying deep learning, neural networks, and other AI methods to extract meaningful insights. The role requires expertise in biology, statistics, and programming languages like Python or R.

What are the typical daily responsibilities for someone in a Bioinformatics Machine Learning position?

In a Bioinformatics Machine Learning role, your daily tasks usually involve developing and tuning machine learning models to analyze large biological datasets, such as genomics or proteomics data. You'll collaborate closely with researchers, biologists, and data scientists to understand project goals, interpret results, and refine analytical approaches. Routine work includes coding, troubleshooting algorithms, visualizing data outputs, and documenting findings for internal teams or publication. The role often requires balancing independent analysis with teamwork and regular communication across disciplines, making it both technically challenging and highly collaborative.

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

A successful Bioinformatics Machine Learning professional needs a solid background in biology, statistics, and computer science, often backed by an advanced degree such as a Master's or PhD in bioinformatics, data science, or a related field. Proficiency with programming languages like Python or R, experience with machine learning libraries (e.g., TensorFlow, scikit-learn), and knowledge of version control systems are typical requirements, and relevant certifications can be beneficial. Strong problem-solving abilities, effective communication skills, and the capacity to work collaboratively in interdisciplinary teams set candidates apart. These skills are crucial for designing robust computational models, interpreting complex biological data, and translating findings into actionable insights in research or clinical settings.

What are the most commonly searched types of Bioinformatics Machine Learning jobs in Texas? The most popular types of Bioinformatics Machine Learning jobs in Texas are:
What are popular job titles related to Bioinformatics Machine Learning jobs in Texas? For Bioinformatics Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Bioinformatics Machine Learning jobs in Texas look for? The top searched job categories for Bioinformatics Machine Learning jobs in Texas are:
Bioinformatics Machine Learning jobs near you
Infographic showing various Bioinformatics Machine Learning job openings in Texas as of July 2026, with employment types broken down into 1% Locum Tenens, 51% As Needed, 31% Full Time, 4% Part Time, 11% Nights, and 2% Summer. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.
Postdoctoral Associate

Postdoctoral Associate

Baylor College of Medicine

Houston, TX • On-site

$62K/yr

Full-time

Re-posted 15 days ago

Baylor College of Medicine rating

8.6

Company rating: 8.6 out of 10

Based on 21 frontline employees who took The Breakroom Quiz

56th of 553 rated colleges and universities

Job description

Postdoctoral Associate
Division: Medicine
Work Arrangement: Onsite only
Location: Houston, TX
Salary Range: $62,232
FLSA Status: Exempt
Work Schedule: Monday - Friday, 8 a.m. - 5 p.m.
Summary
The Cheng Lab in the Department of Medicine, Section of Epidemiology & Population Sciences is searching for highly motivated and talented Postdoctoral Associate to work on Bioinformatics and Computational Biology in Cancer Genomics and Immunology. This position will be involved in the development and/or application of computational approaches to understand the mechanism of cancer development, progression, metastasis, and prognosis. The Postdoc should have experience in processing and analyzing data from the next-generation sequencing and single-cell technologies (e.g., scRNA-seq, scATAC-seq, scTR-seq, and/or spatial transcriptomics). Previous experience in both Bioinformatics/Genomics and Cancer Biology is desirable. The Postdoc is expected to collaborate closely with experimental biologists and clinicians. The position offers an extraordinary team-based science environment with opportunities for significant education, training, and career development. The role includes designing analyses, interpreting complex datasets, and translating findings into biologically meaningful insights within a multidisciplinary research environment. This position offers a highly collaborative, team-based setting with opportunities for advanced training, mentorship, and career development.
Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.
Job Duties
  • Develops and/or applies computational approaches to understand the mechanism of cancer development, progression, metastasis, and prognosis.
  • Collaborates closely with experimental biologists and clinicians to design studies, validates computational findings, and supports translational applications of research discoveries.
  • Participates in regular joint meetings to present findings, discuss ongoing projects, and align research strategies with lab and departmental priorities.
  • Assists in mentoring and training of junior researchers in computational techniques and bioinformatics best practices.
  • Translates complex computational results into biologically meaningful insights in collaboration with wet-lab teams.
  • Maintains thorough documentation of analyses, pipelines, and datasets in version-controlled environments.
  • Collaborates with experimental biologists and clinicians to apply computational and bioinformatics approaches to study cancer development, progression, metastasis, and prognosis.
  • Performs other job-related duties as assigned.

Minimum Qualifications
  • MD or Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.

Preferred Qualifications
  • PhD in Computational Biology, Bioinformatics, or a related field (e.g. statistics, computer science, or quantitative biology).
  • Knowledge of basic molecular biology, genomics, and epigenetics.
  • Experience in processing and analyzing data from the next-generation sequencing and single-cell technologies (e.g., scRNA-seq, scATAC-seq, scTR-seq, and/or spatial transcriptomics).
  • Experience in both Bioinformatics/Genomics and Cancer Biology is desirable.
  • Experience in the application and development of computational methods/tools or machine learning algorithms.
  • Good computer programming skills in R/Matlab/PerlPython.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.
PD; SN
Requisition ID: 25323

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