Job Location: Lemont, IL
The Data Science and Learning Division at the Argonne National Laboratory has an opening for a postdoctoral researcher to develop computational methodologies for comparing deep learning drug response models to advance precision oncology. This is a multi-year project in collaboration with the Frederick National Laboratory for Cancer Research (FNLCR) and makes use of large GPU clusters, advanced AI accelerators, and DOE leadership class supercomputers.
The successful candidate will work in an interdisciplinary team to develop a computational framework for comparing and improving deep learning models for drug response prediction. Prediction of cancer treatment response using machine learning algorithms holds great promise for the future of precision oncology. While various deep learning techniques have been explored for this purpose, the lack of a rigorous framework for comparing models and evaluating their potential utility in clinical practice impedes the adoption of these methods in mainstream cancer research. By leveraging the knowledge of deep learning and cancer biology you will collaborate with scientists in developing a framework for large-scale analyses of deep learning models. You will develop workflows that will ultimately enable personalized recommendations of treatments, drug repurposing, and new drug development. The successful candidate will be an integral part of a large group of computational scientists in the Data Science and Learning division.
Responsibilities:
- Develop methodologies to assess the generalizability, reproducibility, performance, and robustness of deep learning models for drug response prediction
- Propose novel data-driven approaches to improve existing drug response prediction models to advance the personalized treatment of cancer
- Implement large-scale workflows for deep learning, including hyperparameter optimization (HPO), neural architecture search (NAS), and distributed model training in high-performance computing (HPC) environments
- Establish benchmark datasets and evaluation criteria to assess the performance of deep learning models for precision oncology
- Participate in hackathons with project collaborators and the broader community of deep learning developers and cancer researchers
- Publish and present findings based on comparisons of community-developed drug response prediction models
Position Requirements
- Recent or soon-to-be completed Ph.D. (typically within 0-3 years) in computer science, electrical engineering, statistics, bioinformatics, computational biology, or related fields
- Comprehensive experience implementing and evaluating deep learning models that use open-source frameworks such as TensorFlow (Keras), PyTorch, or MXNet (Gluon)
- Knowledge of deep learning methods such as transfer learning, multi-task learning, and auto encoders
- Experience with Python machine learning modules such as scikit-learn, numpy and pandas
- Experience with version control including Git and GitHub
- Ability to communicate research outcomes through presentations and publications, present findings at conferences, and participate in the development of research proposals
- Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork
- Experience applying statistical analysis and machine learning for cancer research
- Experience installing deep learning frameworks in a Linux operating system environment
- Experience building containers with singularity and/or docker
- Experience with data analysis and programming in high-performance computing (HPC) environment
- Experience curating and analyzing cancer multi-omics data (e.g., genomics, transcriptomics)
Job Family
Postdoctoral Family
Job Profile
Postdoctoral Appointee
Worker Type
Long-Term (Fixed Term)
Time Type
Full time
As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law.
Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne’s Legal Department.
All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment.
Please note that all Argonne employees are required to be vaccinated against COVID-19. All successful applicants will be required to provide their COVID-19 vaccination verification as a condition of employment, subject to limited legally recognized exemptions to COVID-19 vaccination.
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