Predoctoral Appointee – Machine Learning Viral Glycosylation Prediction

Key Responsibilities

• Design, develop, and evaluate machine learning algorithms for predicting glycosylation sites, glycosylation patterns, and glycan occupancy across diverse viral proteins.
• Develop graph neural network (GNN) architectures and evaluate alternative deep learning approaches for learning sequence- and structure-based representations of viral proteins.
• Integrate protein sequence, structural, evolutionary, and biochemical datasets to build high-quality training and benchmarking datasets.
• Design data preprocessing, feature engineering, model training, hyperparameter optimization, and benchmarking workflows for large biological datasets.
• Implement scalable software pipelines using modern machine learning frameworks (e.g., PyTorch, PyTorch Geometric, DGL, JAX, or Tensor Flow) and maintain reproducible computational workflows.
• Optimize and deploy machine learning workflows on Argonne’s leadership-class high-performance computing systems using distributed computing techniques where appropriate.
• Evaluate model performance using rigorous statistical analyses and compare newly developed methods against existing computational approaches.
• Collaborate closely with computational biologists, structural biologists, virologists, and computer scientists to interpret computational predictions and refine modeling strategies.
• Document software, computational workflows, datasets, and research findings to ensure reproducibility and long-term maintainability.
• Present research progress during project meetings, seminars, and laboratory reviews.
• Contribute to manuscripts, technical reports, conference presentations, and open-source software releases where appropriate.
• Participate in collaborative research initiatives across CELS and other Argonne divisions while adhering to laboratory policies and best practices for scientific software development.
• Perform additional research-related duties assigned by the supervisor that support project objectives and professional development.

• Development of novel machine learning methods for viral glycosylation prediction.
• Implementation of scalable and reproducible computational workflows suitable for execution on leadership-class supercomputing systems.
• Validation and benchmarking of computational models against experimental and public datasets.
• Contributions to peer-reviewed publications, technical reports, and scientific presentations.
• Effective collaboration across multidisciplinary research teams.
• Development of reusable software and computational tools that support future research within CELS and the broader scientific community.

• Recently completed Master's degree
• Demonstrated experience developing machine learning or deep learning models.
• Proficiency in Python and scientific programming.
• Experience with one or more deep learning frameworks such as PyTorch, Tensor Flow, or JAX.
• Experience working with biological sequence, structural, or other scientific datasets.
• Familiarity with software engineering best practices including version control (Git), testing, and reproducible computational workflows.
• Strong analytical, problem-solving, and communication skills.
• Ability to work effectively in interdisciplinary research teams.
• Ability to model Argonne's core values of impact, safety, respect and teamwork.

• Experience developing graph neural networks or geometric deep learning methods.
• Experience with protein language models, structural biology, bioinformatics, or computational genomics.
• Familiarity with glycosylation biology, glycobiology, or post-translational modifications.
• Experience using high-performance computing systems, GPUs, distributed training, or parallel computing.
• Experience with scientific visualization and statistical analysis.
• Record of publications, conference presentations, or open-source software contributions.
• Familiarity with cloud computing or large-scale AI infrastructure.

• Job Family:
  Temporary
• Job Profile:
  Predoctoral Appointee
• Worker Type:
  Long-Term (Fixed Term)
• Time Type:
  Full time
• Expected hiring range for this position is $58,297.00-$97,161.00.
• Please note that the pay range…