Postdoctoral Associate (Public Health Sciences)

Key Responsibilities

• Develop, implement, and evaluate machine learning and deep learning models for biological data analysis
• Work with large-scale datasets such as: Genomics / transcriptomics (bulk, single-cell, spatial), Epigenomics, proteomics, or multi-omics data
• Design data preprocessing, feature engineering pipelines
• Implement models in Python using frameworks such as PyTorch, TensorFlow
• Perform statistical analyses and model benchmarking against existing methods
• Collaborate with wet-lab scientists and domain experts to interpret results
• Contribute to manuscripts, conference abstracts, and presentations

Required Qualifications

• Ph.D in Computer Science, Bioinformatics, Computational Biology, Data Science, or a related field
• Strong programming skills in Python, R
• Solid understanding of: Machine learning fundamentals, Deep learning architecture (e.g., CNNs, RNNs, Transformers), Optimization and model evaluation
• Knowledge in biology and genomics
• Experience working with large datasets and numerical computing libraries (NumPy, Pandas, SciPy)
• Familiarity with Linux environments, version control (Git), cloud computing
• Prior research experience with publications or preprints

Preferred Qualifications

• Prior experience in bioinformatics or computational biology
• Experience with Single-cell or spatial omics analysis, DNA Methylation analysis, Graph neural networks, Generative models (VAEs, diffusion models), Multi-modal learning
• Knowledge of bioinformatics tools and data formats (FASTQ, BAM, VCF, H5AD, AnnData, Seurat, etc.)
• Experience with HPC clusters, cloud computing, or GPU acceleration
• Strong mathematical background in linear algebra, probability, and statistics

Benefits

The University of Miami offers competitive salaries and a comprehensive benefits package including medical, dental, tuition remission and more.