We are seeking a highly motivated Postdoctoral Researcher with expertise in computational biology, deep mutational scanning data, and generative artificial intelligence (AI). The successful candidate will work on cutting-edge research integrating genome-scale language models (GenSLMs) with deep mutational scanning data, and experimental virology to predict viral evolution and identify emerging pathogen variants. You will be part of a collaborative initiative between Argonne National Laboratory (ANL), Lawrence Livermore National Laboratory (LLNL), Fred Hutchinson Cancer Center (FCC), University of Pittsburgh, University of Texas Medical Branch and BARDA, aimed at advancing pandemic bio-preparedness through AI-driven forecasting.

With advances in machine learning frameworks and emerging accelerator technology integrated into the latest supercomputers, unique opportunities exist to significantly improve the time-to-solution for monitoring emerging pathogen variants. Our group has made several novel contributions in:

• Building novel generative models for predicting genome-scale evolutionary patterns using GenSLMs
• Developing scalable models that can, when integrated with high throughput molecular dynamics simulations, predict emerging and new variants of interest in SARS-CoV-2
• Integrating high-throughput deep mutational scanning and reverse genetics workflows to support pandemic bio-preparedness.

As part of this team, you will obtain hands-on training and experience in developing state-of-the-art machine learning and AI models on these platforms while contributing extensively to discovering novel lead molecules that can be validated via our collaborative partners.

In this role you can expect to:

• Develop and refine generative AI models for predicting viral evolution, including SARS-CoV-2 and influenza A/H3N2.
• Integrate deep mutational scanning data to assess viral fitness and immune escape, collaborating with experimental virologists.
• Work with large-scale genomic and proteomic datasets from BV-BRC, GISAID, and other sources to train and validate AI models.
• Develop computational workflows incorporating LLMs, Monte Carlo Tree Search (MCTS), phylogenetic inference, uncertainty quantification, and epidemiological modeling.
• Perform predictive modeling using high-performance computing (HPC) infrastructure.
• Validate computational predictions by collaborating with experimental groups conducting reverse genetics studies and structural biology analyses.
• Publish findings in high-impact journals and present research at leading conferences.

Position Requirements

Required skills, knowledge and skills:

• Recent or soon-to-be completed Ph.D. within the last 5 years in Computational Biology, Bioinformatics, Machine Learning, Artificial Intelligence, Virology, or a related field
• Strong programming skills in Python, R, or Julia, with experience in deep learning frameworks (TensorFlow/PyTorch)
• Experience with large-scale genomic/proteomic datasets and machine learning applied to biological sequences
• Knowledge of phylogenetics, protein structure-function analysis, and viral evolution
• Familiarity with deep mutational scanning datasets and methods for quantifying viral immune escape
• Strong publication record in relevant fields
• Ability to work independently and collaboratively in a multidisciplinary research environment
• Ability to model Argonne's core values of impact, safety, integrity, safety and teamwork

Preferred skills, knowledge and skills:

• Experience with generative models (transformers, diffusion models, VAEs, GANs) applied to biological sequences
• Familiarity with the theory behind modern molecular biology techniques
• Knowledge of HPC environments, cloud computing, or GPU-accelerated machine learning
• Background in Monte Carlo Tree Search (MCTS) or reinforcement learning for sequence generation
• Familiarity with biological sequence alignment tools (MAFFT, FastTree, RAxML-NG)
• Experience with protein structure modeling (AlphaFold, Rosetta, ESMFold)

Job Family

Postdoctoral

Job Profile

Postdoctoral Appointee

Worker Type

Long-Term (Fixed Term)

Time Type

Full time

The expected hiring range for this position is $70,758.00 - $110,379.55.

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.