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

As part of a collaborative initiative with the University of Chicago Comprehensive Cancer Center, the Postdoctoral Researchers will work closely with a multidisciplinary team of computational and experimental biologists. The team is dedicated to developing innovative therapeutic strategies for targeting IDPs, including biologics such as protein-protein inhibitors, Proteolysis-targeting chimeras (PROTACs), nanobodies, and more.

In this role you can expect to:

• 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.

You will have access to state-of-the-art computing resources, including:

• NVIDIA DGX-2 Systems: Powerful platforms for AI and deep learning (details: NVIDIA DGX-2).
• Intel-based Aurora Supercomputer: A next-generation supercomputing system (details: Aurora Supercomputer).
• Additional advanced compute architectures designed for machine learning and AI workflows.

Collaborative facilities:

• In addition to computational resources, the postdoctoral researcher will have access to dedicated wet-lab facilities at the University of Chicago and Argonne National Laboratory’s Biosciences Division, allowing for seamless computational and experimental research integration.

Position Requirements

• Completed PhD within the last 0-5 years in Biology or a related field.
• Ability to model Argonne's core values of impact, respect, integrity, safety and teamwork.
• Computational Biology: Strong background in systems biology and regulatory network modeling.
• Interdisciplinary Collaboration: Experience working across disciplines with computational biologists, computer scientists, and experimental biologists.
• Assay Expertise: Functional understanding of quantitative and high-throughput assays, particularly in biological signaling and screening contexts.
• Machine Learning & Statistics: Proficiency in machine learning, statistical modeling, and quantitative methods for multi-omics data analysis.
• Molecular Simulations: Expertise with molecular simulation tools like OpenMM, AMBER, Gromacs, and NAMD.
• Deep Learning Development: Experience developing, validating, and deploying deep learning models, especially using Pytorch.
• Multi-Omic Data Representation: Ability to build deep representations of multi-omic data.
• Programming: Strong knowledge of Python, C/C++, Julia, and other relevant programming languages.

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