Postdoctoral Associate - AI for Brain Tumors

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 Develop and implement AI models for perturbation prediction Design, train, and evaluate 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 data Process and harmonize scRNA-seq, scATAC-seq, and related datasets across brain tumor cohorts. Perform downstream analyses such as cell state annotation, pathway enrichment, and tumor–tumor microenvironment interaction modeling to generate biologically meaningful insights. Lead computational research projects and method development. Perform 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. #J-18808-Ljbffr Baylor College of Medicine