Postdoctoral Appointee - Computational and Systems Biology

Overview

The Data Science Learning Division at Argonne National Laboratory is seeking a postdoctoral researcher to conduct cutting‑edge computational and systems biology research. The primary focus of this role is to explore how intrinsically disordered proteins (IDPs) mediate signaling mechanisms, with particular emphasis on cancer therapeutics. Supported by a multi‑year ARPA‑H grant, this project aims to revolutionize the development of therapeutic platforms for IDPs, creating significant advancements in cancer research and treatment strategies.

The postdoctoral researcher will work closely with a multidisciplinary team of computational and experimental biologists at the University of Chicago Comprehensive Cancer Center and Argonne National Laboratory.

• Develop foundational models to describe IDP interactions under various physiological conditions, both normal and cancer related.
• Use these models to iteratively design, validate, and refine experiments, leading to effective therapeutic strategies targeting IDPs.
• Collaborate on the development of open‑source machine learning tools to support these therapeutic designs.
• Work closely with high‑throughput screening teams at the University of Chicago, automating screening protocols in partnership with Argonne National Laboratory.
• Drive research at the intersection of automation, robotics, generative AI, and computational simulations, leveraging the latest advancements in computing infrastructure.

• Exercise independent judgment in research activities and possess strong writing skills.
• Gain experience developing machine learning models at a world‑class high‑performance computing facility.

• Access to NVIDIA DGX‑2 systems for AI and deep learning.
• Use of the Intel‑based Aurora supercomputer and other advanced compute architectures designed for machine learning and AI workflows.
• Access to dedicated wet‑lab facilities at the University of Chicago and Argonne National Laboratory’s Biosciences Division for seamless computational and experimental research integration.

• Recent or soon to be completed PhD (0–5 years) in Computational Biology or related field.
• Strong background in systems biology and regulatory network modelling.
• Experience working across disciplines with computational biologists, computer scientists, and experimental biologists.
• Functional understanding of quantitative and high‑throughput assays, particularly in biological signalling and screening contexts.
• Proficiency in machine learning, statistical modelling, and quantitative methods for multi‑omics data analysis.
• Expertise with molecular simulation tools such as OpenMM, AMBER, Gromacs, and NAMD.
• Experience developing, validating, and deploying deep learning models, especially using PyTorch.
• Ability to build deep representations of multi‑omic data.
• Strong programming knowledge in Python, C/C++, Julia, and other relevant languages.
• Alignment with Argonne's core values of impact, safety, respect, integrity, and teamwork.

The expected hiring range for this position is $70,758.00–$. The pay offered to a selected candidate will be determined based on qualifications, scope of responsibilities, internal equity, and external market pay for comparable jobs. Comprehensive benefits are part of the total rewards package.

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. Argonne is an equal‑employment‑opportunity employer and is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation.