The Data Science and Learning Division at the Argonne National Laboratory has an opening for a postdoctoral researcher to develop and apply novel machine learning (ML) and deep learning (DL) methods for anti-cancer drug response prediction. Our goal is to develop a generalized model that can predict drug response across a range of tumor types and drug candidates. Multiple types of data, such as tumor genomic data, tumor histology images, drug molecular structures, and information about cancer and treatment mechanisms, need to be integrated to build the comprehensive prediction model. The successful candidate will work in a multi-disciplinary and collaborative environment consisting of computational scientists, experimental and computational biologists, and clinicians. The Argonne Nation Laboratory provides abundant computational resources for the research project, including workstations, clusters, and supercomputers. Candidates can apply for this position at the Argonne Careers website (https://www.anl.gov/hr/postdoctoral-applicants) or send CVs to Dr. Yitan Zhu (email@example.com).
Responsibilities: The successful candidate will research and develop novel ML/DL models for predicting anti-cancer drug response, which integrate multiple data modalities, alternative data representations, and information about disease and treatment mechanisms. The research will also focus on the development of transfer learning workflows, which utilize data generated on model biological systems such as patient-derived xenografts and organoids to improve the performance of drug response prediction for designing patient treatments and/or high-profile pre-clinical drug screening experiments. The successful candidate will be an integral part of a large group of computational scientists in the Data Science and Learning division.
· A recent or soon-to-be completed PhD in computer science, electrical engineering, bioinformatics, computational biology, or related fields.
· Comprehensive experience of programming in one or more programming languages such as Python and R.
· Knowledge of ML/DL methods and frameworks, such as deep neural network, encoder/decoder, gradient boosting machine, multi-modal learning, transfer learning, and others.
· Experience of using ML/DL tools and packages, such as TensorFlow, PyTorch, Keras, and scikit-learn.
· Ability to draft manuscripts for scientific publications based on research outcomes, to present findings at major conferences, and to participate in the development of new research proposals.
· Ability to model Argonne’s Core Values: Impact, Safety, Respect, Integrity, and Teamwork.
· Knowledge in bioinformatics and experience of processing and analyzing genomic data, such gene set enrichment analysis and regulation/interaction network analysis.
· Background in cancer research, such as cancer mechanism study, disease gene identification, and histology image analysis.
· Background in drug development, such as drug response prediction and small molecule lead discovery and optimization.
· Knowledge of parallel computing and high-performance computing.
· Excellent communication skills for research collaboration with biologists and clinicians.
Tagged as: Computer Science, Life Sciences
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