Postbaccalaureate Appointee – Machine Learning for Viral Glycosylation Prediction

The Computing, Environment, and Life Sciences (CELS) directorate at Argonne National Laboratory is seeking a Post-Bachelor Appointee to contribute to research at the intersection of artificial intelligence, computational biology, and high-performance computing.

• The successful candidate will join an interdisciplinary team developing machine learning approaches to understand glycosylation patterns across viral proteins, supporting research that advances computational methods for pathogen characterization, vaccine design, and therapeutic discovery.
• Working under the guidance of experienced computational scientists, the appointee will assist in the development, implementation, validation, and evaluation of machine learning models for predicting glycosylation sites and glycan occupancy in viral proteins.
• The position offers an opportunity to develop technical expertise in machine learning, computational biology, scalable software development, and scientific computing while gaining experience in a collaborative national laboratory research environment.

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In this role, you can expect to:

• Assist in the development, implementation, and evaluation of machine learning models for predicting glycosylation sites and glycosylation patterns in viral proteins.
• Support the design and implementation of graph neural network (GNN) models and other deep learning approaches for learning sequence- and structure-based representations of viral proteins.
• Collect, curate, preprocess, and integrate biological sequence, structural, and experimental datasets used for model development and benchmarking.
• Develop software tools and computational workflows using modern machine learning frameworks such as PyTorch, PyTorch Geometric, TensorFlow, or related libraries.
• Conduct model training, validation, benchmarking, and performance analysis using appropriate statistical and computational evaluation methods.
• Assist in deploying and optimizing machine learning workflows on Argonne's high-performance computing systems.
• Document software, datasets, computational workflows, and experimental results to promote reproducibility and maintainability.
• Collaborate with computational scientists, biologists, and software engineers to interpret model predictions and improve computational methods.
• Prepare technical reports, presentations, and documentation summarizing research progress and computational results.
• Contribute to manuscripts, conference presentations, software releases, and other research dissemination activities as appropriate.
• Participate in project meetings, technical discussions, and collaborative research activities across multidisciplinary teams.
• Perform additional research and technical duties assigned by the supervisor in support of project objectives.

Expected Outcomes:

• Success in this position will be demonstrated through:
• Development of reproducible computational workflows supporting machine learning research on viral glycosylation.
• Successful implementation and evaluation of machine learning models under the guidance of project scientists.
• Contribution to scalable software and computational tools supporting ongoing research activities.
• Effective collaboration within multidisciplinary teams.
• Preparation of high-quality technical documentation, reports, and research presentations.
• Growth in technical and research capabilities that prepare the appointee for graduate study or advanced research positions.

Position Requirements

Required Qualifications:

• Recently completed Bachelor's degree in Computer Science, Bioinformatics, Computational Biology, Data Science, Biomedical Engineering, Applied Mathematics, or a related STEM discipline.
• Experience programming in Python or a similar scientific programming language.
• Basic knowledge of machine learning or deep learning methods.
• Familiarity with one or more machine learning frameworks such as PyTorch, TensorFlow, or JAX.
• Experience analyzing scientific or biological datasets through coursework, research projects, or internships.
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills.
• Demonstrated ability to work effectively both independently and as part of an interdisciplinary research team.
• Ability to model Argonne's core values of impact, safety, respect, teamwork, ang integrity.

Preferred Qualifications:

• Undergraduate research experience in machine learning, computational biology, bioinformatics, or related fields.
• Experience with graph neural networks or representation learning.
• Familiarity with protein sequence analysis, structural biology, glycobiology, or bioinformatics.
• Experience using Linux environments, Git, and software development best practices.
• Exposure to GPU computing, high-performance computing, or cloud computing environments.
• Experience presenting research findings or contributing to scientific publications or open-source software projects.

Job Family

Temporary

Job Profile

Postbaccalaureate Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

The expected hiring range for this position is $58,656.00-$92,273.00.

Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.