Argonne National Laboratory
Postdoctoral Appointee - Computational and Systems Biology
Argonne National Laboratory, Lemont, Illinois, United States, 60439
Postdoctoral Appointee – Computational and Systems Biology
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Postdoctoral Appointee – Computational and Systems Biology
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Argonne National Laboratory .
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 will be exploring how intrinsically disordered proteins (IDPs) mediate signaling mechanisms, with a 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.
As part of a collaborative initiative with the University of Chicago Comprehensive Cancer Center, the postdoctoral researcher will work closely with a multidisciplinary team of computational and experimental biologists to develop innovative therapeutic strategies for targeting IDPs, including biologics such as protein‑protein inhibitors, Proteolysis‑targeting chimeras (PROTACs), nanobodies, and more.
Key Responsibilities
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.
Additional Responsibilities
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.
Computing Resources
NVIDIA DGX‑2 Systems: Powerful platforms for AI and deep learning.
Intel‑based Aurora Supercomputer: A next‑generation supercomputing system.
Additional advanced compute architectures designed for machine learning and AI workflows.
Dedicated wet‑lab facilities at the University of Chicago and Argonne National Laboratory’s Biosciences Division for seamless computational and experimental research integration.
Position Requirements
A recent or soon to be completed PhD within the last 0‑5 years.
Strong background in systems biology and regulatory network modeling.
Experience working across disciplines with computational biologists, computer scientists, and experimental biologists.
Functional understanding of quantitative and high‑throughput assays, particularly in biological signaling and screening contexts.
Proficiency in machine learning, statistical modeling, and 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 knowledge of Python, C/C++, Julia, and other relevant programming languages.
Alignment with Argonne’s core values of impact, safety, respect, integrity, and teamwork.
Location: Lemont, IL. Salary: $40,000 – $45,000. Posted 2 weeks ago.
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Postdoctoral Appointee – Computational and Systems Biology
role at
Argonne National Laboratory .
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 will be exploring how intrinsically disordered proteins (IDPs) mediate signaling mechanisms, with a 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.
As part of a collaborative initiative with the University of Chicago Comprehensive Cancer Center, the postdoctoral researcher will work closely with a multidisciplinary team of computational and experimental biologists to develop innovative therapeutic strategies for targeting IDPs, including biologics such as protein‑protein inhibitors, Proteolysis‑targeting chimeras (PROTACs), nanobodies, and more.
Key Responsibilities
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.
Additional Responsibilities
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.
Computing Resources
NVIDIA DGX‑2 Systems: Powerful platforms for AI and deep learning.
Intel‑based Aurora Supercomputer: A next‑generation supercomputing system.
Additional advanced compute architectures designed for machine learning and AI workflows.
Dedicated wet‑lab facilities at the University of Chicago and Argonne National Laboratory’s Biosciences Division for seamless computational and experimental research integration.
Position Requirements
A recent or soon to be completed PhD within the last 0‑5 years.
Strong background in systems biology and regulatory network modeling.
Experience working across disciplines with computational biologists, computer scientists, and experimental biologists.
Functional understanding of quantitative and high‑throughput assays, particularly in biological signaling and screening contexts.
Proficiency in machine learning, statistical modeling, and 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 knowledge of Python, C/C++, Julia, and other relevant programming languages.
Alignment with Argonne’s core values of impact, safety, respect, integrity, and teamwork.
Location: Lemont, IL. Salary: $40,000 – $45,000. Posted 2 weeks ago.
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