Postdoctoral Appointee in Biology

Argonne National Laboratory

Institution: Argonne National Laboratory (in collaboration with the University of Chicago Comprehensive Cancer Center)
Division: Data Science Learning Division
Employment Type: Full-time, Long-Term Fixed Term
Salary Range: $70,758 – $117,925 (based on experience and qualifications)
Application Contact: careers@anl.gov, +1 630-252-2336

Position Overview

The postdoc will conduct computational and systems biology research focused on intrinsically disordered proteins (IDPs) and their role in cancer signaling and therapeutics. The project is supported by a multi-year ARPA-H grant and aims to develop innovative therapeutic strategies, including PROTACs, nanobodies, and protein-protein inhibitors.

The position integrates computational modeling, high-throughput experimental work, and AI-driven approaches. The postdoc will collaborate with a multidisciplinary team at Argonne National Laboratory and the University of Chicago.

Key Responsibilities

  • Develop models of IDP interactions under normal and cancer-related conditions.

  • Design, validate, and refine experiments to guide therapeutic development targeting IDPs.

  • Collaborate on open-source machine learning tools for therapeutic design.

  • Interface with high-throughput screening teams, automating protocols.

  • Leverage advanced computing infrastructure for simulations, automation, and AI-driven research.

  • Exercise independent judgment in research and contribute to writing and publications.

Research & Computing Resources

  • NVIDIA DGX-2 Systems – AI and deep learning platforms

  • Aurora Supercomputer – Intel-based next-gen HPC system

  • Additional compute architectures for machine learning and AI

  • Wet-lab facilities at Argonne and University of Chicago for integrated computational-experimental studies

Required Qualifications

  • PhD (0–5 years post-completion) in computational biology, systems biology, bioinformatics, or related fields

  • Expertise in systems biology, regulatory network modeling, and multi-omics data

  • Experience with interdisciplinary collaboration (computational and experimental biologists)

  • Familiarity with high-throughput assays and quantitative biological screening

  • Proficiency in machine learning, statistical modeling, Python, C/C++, Julia

  • Experience with molecular simulations (OpenMM, AMBER, Gromacs, NAMD)

  • Deep learning experience, especially with PyTorch

  • Ability to model Argonne’s core values: impact, safety, respect, integrity, and teamwork

Preferred Experience

  • Developing multi-omic data representations

  • Generative AI and automation in experimental design

  • Translational cancer research and therapeutic development

Additional Information

  • Argonne offers a safe, collaborative, and inclusive workplace

  • Employment contingent on background check and, if required, government access authorization

  • Access to world-class HPC, AI infrastructure, and wet-lab facilities

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