Description: The Gevaert lab is recruiting imaging genomics postdocs. The postdoc will be involved in the development of and application of computational methods, from data integration to statistical analysis and machine learning and to learn patterns from biomedical image data. Potential focus areas are multi omics data fusion, machine learning and deep learning.
This work fits within the overall goal of the Gevaert lab in multi-scale data fusion whereby the postdoc will work with other lab members working on cellular and tissue level data (e.g. MR, CT imaging) towards the long term goal of modeling cancer at multiple scales. The postdoc will be embedded within a multi-disciplinary environment involving clinicians, molecular biologists, statisticians and mathematicians. For more on multi-scale data fusion in the Gevaert lab see http://gevaertlab.stanford.edu/.
Postdocs ideally have a mixture of the following skills:
* Ph.D. with a strong background in medical image processing, quantitative imaging, or related field
* Proven track record in either R programming or python. Proficiency in other programming environments is a plus
* Familiarity with major machine learning tools, either expert in one framework or able to work with multiple frameworks such as support vector machines, Bayesian methods, regularized regression, decision tree, deep learning, ...
* Excellent communication skills and fully fluent spoken and written English
* Strong problem-solving skills, creative thinking, and the ability to build new software tools as needed
* Proven experience in medical image processing software platforms.
Application Instructions: Please contact Olivier Gevaert (ogevaert at stanford point edu) and send your cover letter, cv and names of three references to apply for this position. This position is available immediately.