Full-Time

Postdoctoral Associate

Public Health Sciences

Posted on 5/9/2026

University of Miami

University of Miami

No salary listed

Company Does Not Provide H1B Sponsorship

Miami, FL, USA

In Person

Category
Lab & Research (2)
,
Required Skills
Python
Data Science
Tensorflow
R
Neural Networks
Git
Pytorch
Machine Learning
Pandas
NumPy
Linux/Unix
Requirements
  • 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
  • Solid understanding of deep learning architectures (e.g., CNNs, RNNs, Transformers)
  • Solid understanding of optimization and model evaluation
  • Solid understanding of 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.
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.
Desired Qualifications
  • Prior experience in bioinformatics or computational biology
  • 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)
  • Experience with HPC clusters, cloud computing, or GPU acceleration
  • Strong mathematical background in linear algebra, probability, and statistics
  • Prior research experience with publications or preprints

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