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Responsibilities:

The Computational Biomarker Imaging Group (CBIG) has an open postdoctoral positions. CBIG's mission is to act as a translational catalyst between computational science and cancer imaging research.

Work in our group focuses on developing innovative image analysis, machine learning, AI and data science methodologies for multimodality imaging, and also on incorporating such methods into clinically relevant applications. Most of our work to date has been on breast and lung cancer imaging, while more recently expanding more broadly in oncologic imaging and molecular imaging applications. A priority area is integrating imaging with genomic biomarkers towards integrated precision diagnostics for prevention and therapy for cancer. Applications focusing on radiomics and deep learning are also a priority.

We are seeking highly motivated individuals with excellent academic track-record, including first-author publications in peer-reviewed journals. Applicants should demonstrate excellent oral and written communication skills, and the ability to work effectively independently and as part of a multidisciplinary research team.

Salary range is from $70,000 -$77,000. The salary of the finalist selected for this role will be set based on a variety of factors, including but not limited to departmental budgets, qualifications, experience, education, licenses, specialty, and training. The above hiring range represents the University's good faith and reasonable estimate of the range of possible compensation at the time of posting.

Successful candidates should have, or be in the process of completing, a PhD (or equivalent) in Biomedical, Electrical or Computer Engineering, Computer and Information Science, Applied Mathematics, Statistics/Biostatistics, Bioinformatics, or related field.

Ideal applicants should have a background on biomedical image analysis, computer vision, pattern recognition and/or machine learning. Experience with Bioinformatics is a plus. Proficiency in quantitative analytical methods and computer programming (e.g., Python, C/C ) is essential. Experience with medical image analysis (e.g., MRI, CT, X-ray, Ultrasound) and related statistical methods and software packages (e.g., ITK C/C libraries, R/SPSS) is desired (but not necessary). Experience with Deep Learning is also ideal (e.g., TensorFlow, Keras, PyTorch packages, etc.).