We invite talented post-doctoral candidates with an interest in cancer genomics, multi-omic data analysis, algorithm design, and/or deep learning to apply for a position in Jin Zhang, PhD, MPHS’s translational genomics lab (sites.wustl.edu/jinzhang) at Washington University School of Medicine, St. Louis, MO. This postdoc will also be co-advised by Michael Waters, MD, PhD (www.linkedin.com/in/mike-waters-92a20247/), who will be an Assistant Professor at WashU starting July 2024.
Since joining the WUSTL faculty in 2017, Dr. Zhang has built a lab with an ambitious interest in developing tools and applying cutting-edge bioinformatic and deep learning/AI techniques to advance our understanding of cancer biology and treatment. Our agile, growing research group has been funded by multiple grant awards from the National Cancer Institute, including 2 NCI R01s received last year. We are also extremely proud to be part of the Translational Radiation Oncology Center (TARGET) supported by an NCI U54 Specialized Center grant (sites.wustl.edu/translationalradiation/). The Zhang lab currently has 10 members and is looking to expand to 14 in the next year.
The candidate will enjoy significant autonomy while playing a critical role in finding new discoveries and developing advanced genomic and/or deep learning/AI approaches in cancer biology as applied in personalized radiation therapy (RT). The candidate will also lead projects on multi-omics data integration and clinical imaging data integration. We are aware that most of the candidates won’t be immediately an expert on all the above domains in our groundbreaking multi-disciplinary research. The candidate will enjoy significant mentorship from Drs. Zhang and Waters and experienced lab members to develop their domain knowledge and ability to lead research teams, including guidance in career development and transition to an independent investigator. The position also offers an excellent opportunity to conduct research in a supportive and stimulating environment, closely collaborating with computer scientists, biologists, and clinicians within Department of Radiation Oncology and other institutes across the campus, including the McDonnell Genome Institute, Institute for Informatics, and Siteman Cancer Center.
The Cancer Biology Division within the Department of Radiation Oncology consists of a core group of clinical, physics, and computational science investigators who use both archived and prospectively collected tumor samples to ask fundamental questions about radiotherapy efficacy and cancer biology. The Zhang lab and the Bioinformatics/AI Program work closely with clinicians to identify biomarkers that can be used to stratify patients and to identify patients for targeted therapies.
Primary Duties and Responsibilities:
Trains under the supervision of a faculty mentor including (but not limited to):
1. Develop hypotheses and design novel genomics and/or data science/AI approaches in translational cancer genomics and data integration.
2. Build novel pipelines through developing advanced algorithms and integrating cutting-edge bioinformatics tools using sequencing and other omics data and/or image data.
3. Prepare and present results of research in internal seminars and external conferences and journals.
4. Assist with grant preparation and reporting.
5. Train research assistants in the lab and work with other researchers in applying the informatics tools we develop.
Required Qualifications:
Applicants must have received, or will receive, a Ph.D. by May 31st, 2024, in bioinformatics, genomics, computational biology, computer science, ML/DL/AI, or a related field.
Preferred Qualifications:
1. Experience with bioinformatic algorithm development and/or bioinformatic data analysis.
2. Proficiency in a programming language, such as Python, C++, and/or Perl.
3. Proficiency in a statistical programming language, such as R and SAS.
4. Experience with scientific computing and data visualization tools.
5. Highly motivated with excellent oral and written communication skills.
6. Understanding of current next-generation sequencing technologies.
7. Understanding of statistical techniques for differential expression, gene set enrichment analysis, clustering, outcome analysis, and multivariant analysis.
8. Experience with Git, Docker, Common Workflow Language, and Cloud computing.
9. Deep understanding of concepts and techniques in machine learning/deep learning with Scikit-Learn, Keras, and/or TensorFlow and PyTorch, and/or other cutting-edge AI technologies, including large language model, etc.
Please apply at: wustl.wd1.myworkdayjobs.com/External/job/Washington-University-Medical-Campus/Postdoctoral-Research-Associate--Genomics--Data-Integration--Deep-Learning--AI----Radiation-Oncology_JR81165. You can also send a CV and optionally a cover letter stating research interest to Dr. Jin Zhang: jin.zhang@wustl.edu.