Postdoctoral Appointee – Cancer Drug Discovery Using Machine Learning and Exascale Computing
- Argonne National Laboratory
- Location: Argonne, IL
- Job Number: 7100446 (Ref #: 413408)
- Posting Date: May 20, 2022
- Application Deadline: Open Until Filled
Argonne National Laboratory has an immediate opening for a postdoctoral researcher to work on novel data-driven models and workflows for pre-clinical cancer research that identifies potential small molecule inhibitors of tumor growth. This work will be carried out on our forthcoming exascale supercomputer, Aurora.
The successful candidate will work with an inter-disciplinary team of scientists to develop data-driven techniques using machine learning and deep learning for drug design and predictive models for drug response to help in the treatments of cancer patients. Argonne researchers have developed a scalable workflow for predictive design of small molecules and their activity for COVID-19, and a key goal of this project is to extend and build this workflow for cancer research. Some of the molecules as part of the COVID-19 research are now in various stages of optimization and development, which provides you with an opportunity to interact with a variety of experimental groups at the University of Chicago as well as across the country.
In this role you will:
- Build novel AI-approaches leveraging chemical representations, large language models (for genomic and proteomic data) to suggest design of small molecules targeting various cancer related proteins/biomolecules.
- Use data-driven models for drug design and tumor response prediction to optimize pre-clinical drug screening and drive precision medicine-based treatments for cancer patients.
- Research and develop novel high-throughput workflows to effectively scale the drug-design model on supercomputers.
- Collaborate with scientists in the Argonne Leadership Computing Facility as part of the Aurora Early Science Program to take computational drug screening to a scale only achievable on exascale supercomputers.
- Work in a multi-disciplinary and collaborative environment consisting of computational scientists, experimental and computational biologists, computer scientists, applied mathematicians, and ML/DL experts.
Required skills and qualifications:
- Recent or soon-to-be completed Ph.D. + 0-3 years of experience in computational biology, computer science, biological science, electrical engineering, or related field
- Understanding of basic biochemistry such as protein structure, function, and dynamics
- Experience implementing and evaluating deep learning models in Python, using deep learning frameworks such as Pytorch and/or Tensorflow (Keras)
- Experience running applications in a high-performance computing (HPC) environment
- Solid understanding of python machine learning modules
- Experience implementing complex workflows
- Ability to communicate scientific and technological advances through publications and presentations, and ability to contribute to the development of research proposals
- Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
Preferred skills and qualifications:
- Experience with deep generative models and/or graph-based models
- Experience with small molecule docking and protein receptor molecular dynamic simulations
- Experience applying structure-activity relationship methodologies
Job FamilyPostdoctoral Family
Job ProfilePostdoctoral Appointee
Worker TypeLong-Term (Fixed Term)
Time TypeFull time
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