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.