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Machine Learning Drug Discovery Postdoc information

What is a machine learning drug discovery postdoc?

A Machine Learning Drug Discovery Postdoc is a postdoctoral researcher who uses advanced machine learning techniques to accelerate and improve the drug discovery process. They work at the intersection of computational science, biology, and chemistry to develop algorithms that can predict molecular properties, identify potential drug candidates, and optimize compounds. Their research helps pharmaceutical companies and academic labs find effective drugs more efficiently, often reducing the time and cost required for new drug development. Typically, these postdocs collaborate closely with interdisciplinary teams and may also contribute to scientific publications and conferences.

What are the key skills and qualifications needed to thrive as a machine learning drug discovery postdoc?

To thrive as a Machine Learning Drug Discovery Postdoc, you need a strong background in computational biology, machine learning, and chemistry, typically supported by a PhD in a relevant field. Expertise with programming languages (such as Python or R), deep learning frameworks (like TensorFlow or PyTorch), and bioinformatics tools is highly valuable. Strong analytical thinking, collaboration, and effective scientific communication are crucial soft skills for advancing research projects and sharing results. These skills and qualities are essential to drive innovation, interpret complex biological data, and translate computational models into actionable drug discovery insights.

What are some typical challenges faced by a machine learning drug discovery postdoc, and how can they be addressed?

As a Machine Learning Drug Discovery Postdoc, one of the main challenges is integrating complex biological data with advanced computational models to generate meaningful insights for drug development. Addressing issues such as data sparsity, heterogeneity, and ensuring model interpretability are common hurdles. Collaborating closely with wet-lab scientists, bioinformaticians, and other computational researchers is essential for validating predictions and translating findings into actionable experiments. Regular communication with interdisciplinary teams and staying updated on the latest computational techniques can help overcome these challenges and drive impactful research.

What are popular job titles related to Machine Learning Drug Discovery Postdoc jobs?

For Machine Learning Drug Discovery Postdoc jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Drug Discovery Postdoc job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Postdoctoral Associate

Houston, TX • On-site

Baylor College of Medicine
Colleges, Universities, and Professional Schools • 5 - 10K employees
Learn more about Baylor College of Medicineexternal

$62K/yr

Full-time

Re-posted 14 days ago

Baylor College of Medicine rating

8.0

Company rating: 8.0 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

Job description

Postdoctoral Associate
Division: Pathology
Work Arrangement: Onsite only
Location: Houston, TX
Salary Range: Hiring up to $62,232
FLSA Status: Exempt
Work Schedule: Monday - Friday, 8 a.m. - 5 p.m.
Summary
Postdoctoral positions in cheminformatics are available in the Zhi Tan laboratory at Baylor College of Medicine (Houston, TX), an interdisciplinary group using deep learning, computational chemistry, medicinal chemistry, chemical biology, and molecular cell biology to develop novel therapeutics to tackle complex diseases such as cancers. Postdoctoral Associate with a proven track record in developing open-source machine learning, deep learning, or cheminformatics tools.
Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.
Job Duties
  • Plans, directs and conducts research experiments.
  • Develops research techniques and perform applications required for specific research projects.
  • Conducts literature searches and summarize information in an appropriate format for a particular study.
  • Documents results of experiments and reports to principal investigators.
  • 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
  • Doctoral Degree in Computational Chemistry, bioinformatics, computational biology, or related discipline. Experience may not be substituted in lieu of degree.
  • Experience in cheminformatics software development.
  • Experience in developing machine learning, deep learning tools, especially with application in drug discovery.
  • Experience with high performance computing environment (HPC) / cluster job submission.
  • Knowledge of statistical methods, data science algorithms, scientific and numerical computation.
  • Familiarity with common Python tools including Pandas, Numpy, Scipy, Django, RDKit.
  • Proficiency in coding and debugging in Pytho.
  • Strong knowledge and experience with relational databases (e.g. Oracle, SQL, MySQL).
  • Comfortable working in a Linux environment.
  • Experience with data processing pipelines and data analysis.
  • Excellent communication skills with a diverse team of biological and chemical scientists.
  • Experience with development of open-source computational tools (machine learning, deep learning, cheminformatics), especially with application in drug discovery.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.
Requisition ID: 23583

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