Postdoctoral Appointee - Computational and Systems Biology

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. 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.

• 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

• 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
• 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

• A recent or soon to be completed PhD within the last 0-5 years
• 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 Proficiency:
  Strong knowledge of Python, C/C++, Julia, and other relevant programming languages
• Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork.

Postdoctoral

Postdoctoral Appointee

Long‑Term (Fixed Term)

Full time

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

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…