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Postdoctoral Machine Learning Jobs (NOW HIRING)

The Pennsylvania State University

... Postdoctoral Scholar to conduct research on projects in collaboration with Dr. Sanjay Srinivasan ... machine learning and multipoint geostatistics for characterization of fractures and novel ...

The Pennsylvania State University

The Postdoctoral Scholar will join a Department of Energy (DOE)-funded project focused on the ... The core objective of this research is to advance physics-informed machine learning architectures ...

The Pennsylvania State University

The work will involve research on machine learning, digital twins, electronic design automation ... The postdoc will have the opportunity to mentor graduate and undergraduate students. Guidance will ...

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How much do postdoctoral machine learning jobs pay per year?

As of May 30, 2026, the average yearly pay for postdoctoral 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 Machine Learning job?

A Postdoctoral Machine Learning job is a research-focused position for individuals who have recently earned a Ph.D. in machine learning, artificial intelligence, or a related field. It typically involves conducting advanced research, publishing papers, collaborating with academic or industry partners, and developing novel algorithms or models. These roles are often hosted by universities, research institutes, or tech companies. The position helps researchers gain additional expertise and contribute to cutting-edge advancements before transitioning to faculty, industry, or independent research roles.

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

To thrive as a Postdoctoral Machine Learning researcher, you need a strong background in machine learning theory, statistical analysis, and programming, typically supported by a Ph.D. in computer science, engineering, or a related quantitative field. Experience with Python, TensorFlow, PyTorch, and advanced data analytics tools is highly valued, as are relevant publications and experience with version control systems like Git. Strong problem-solving abilities, clear communication skills, and effective teamwork are crucial to excel in collaborative research settings. These skills and qualities are essential to drive innovative research, efficiently navigate complex datasets, and contribute to impactful scientific discoveries.

What are the typical daily responsibilities of a Postdoctoral Machine Learning researcher?

A Postdoctoral Machine Learning researcher typically spends their day designing and implementing machine learning algorithms, analyzing experimental results, and preparing manuscripts for publication. They often collaborate with interdisciplinary teams of scientists and engineers, attend lab meetings, and contribute to grant writing or project proposals. Regular activities also include keeping up with recent scientific literature, mentoring graduate or undergraduate students, and presenting research findings at conferences or seminars. The blend of technical development and scientific communication makes each day dynamic and offers opportunities to influence both academia and industry.
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Postdoctoral Associate - AI for Brain Tumors

Postdoctoral Associate - AI for Brain Tumors

Baylor College of Medicine

Houston, TX • On-site

$62.23K/yr

Full-time

Posted 5 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

49th of 529 rated colleges and universities

Job description

Job Title: Postdoctoral Associate - AI for Brain Tumors
Division: Neurosurgery
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 Postdoctoral Associate will develop next-generation AI models for large-scale perturbation modeling in brain tumors. The project will involve building and applying state-of-the-art machine learning approaches, including foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single-cell and multi-omic datasets. The goal is to decode tumor cellular heterogeneity and tumor microenvironment interactions, and to identify targetable genes, pathways, and therapeutic strategies at single-cell resolution.
Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.
Job Duties
  • Develops and implements AI models for perturbation prediction:
    • Designs, trains, and evaluates machine learning models (e.g., transformer-based architectures, VAEs, and foundation models) to predict cellular responses to genetic and pharmacologic perturbations. This includes preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing methods.
  • Integrate and analyze large-scale single-cell and multi-omic:
    • Processes and harmonizes scRNA-seq, scATAC-seq, and related datasets across brain tumor cohorts.
    • Performs downstream analyses such as cell state annotation, pathway enrichment, and tumor-tumor microenvironment interaction modeling to generate biologically meaningful insights.
  • Leads computational research projects and method development.
  • 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
  • Ph.D. in Computational Biology, Bioinformatics, Computer Science or a related quantitative field.
  • Strong background in machine learning and statistical modeling, with experience in deep learning frameworks (e.g., PyTorch or TensorFlow). Familiarity with modern architectures such as transformers, variational autoencoders (VAEs), and foundation models is highly desirable.
  • Experience in analyzing large-scale genomics or single-cell datasets (e.g., scRNA-seq, scATAC-seq).
  • Proficiency in Python and experience with R/Seurat or Scanpy.
  • Strong skills in writing efficient, reproducible, and well-documented code.
  • Evidence of productivity through first-author publications or preprints in computational biology, machine learning, or related fields.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.
PD; SN
Requisition ID: 24929

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