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The Itakura Lab has an immediate opening for a creative and motivated postdoctoral scholar to conduct applied research in the areas of machine learning and pattern/feature detection with a focus on either computer vision/image or genomic/molecular data processing and analysis. The lab focuses on implementing machine learning frameworks and radiogenomic approaches on heterogeneous, multi-scale cancer data (e.g., clinical, imaging, histopathologic, genomic, transcriptomic, epigenomic, proteomic) to accelerate discoveries in cancer diagnostics and therapeutics. Projects include prediction modeling of survival and treatment responses, biomarker (feature) discovery, cancer subtype discovery, and identification of new therapeutic targets. Guided by critical and relevant problems in oncology, these projects have the potential to lead to clinically actionable or translatable findings.
The successful candidate will join the Department of Medicine, Division of Oncology and work. The job description:
• Build and implement algorithms in machine learning applied to either imaging data (computer vision) or genomic/molecular data (computational biology)
• Develop software tools for integrative analysis of heterogeneous, multi-omic cancer data using machine learning
• Publish and present research findings in journals and conferences
• PhD (or MD/PhD) in Computer Science, Engineering, Informatics, Statistics, Applied Physics, or a related field with strong skills in data mining, machine learning, or statistics
• Experience in modeling, integrative analyses, parallel computing, and/or software development desirable
• Biomedical knowledge or research experience is not a requisite
• Demonstrated ability to work independently, problem-solve, author manuscripts, strive for innovation, and be highly self-motivated
• Strong interpersonal and communication skills, and ability to work as part of a multi-disciplinary team
- Research statement
- A list of three references with contact information