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Bioinformatics postdoc position to develop computational methods for multi-scale data fusion.

The Gevaert lab is recruiting bioinformatics postdocs. The postdoc will be involved in the development of and application of computational methods, from data integration to statistical analysis and machine learning, to learn patterns in multi omics data. Potential focus areas are multi omics data fusion, epigenomics and deep learning. The lab uses open source programming environments to disseminate methods.

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/.

Qualifications

Postdocs ideally have a mixture of the following skills:

Ph.D. with a strong background in bioinformatics, computational biology, biostatistics, and genomics or proteomics

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 either RNA sequencing analysis, machine learning, deep learning or multi-omics data fusion

Please contact Olivier Gevaert (ogevaert@stanford.edu) and send your cover letter, cv and names of three references to apply for this position. This position is available immediately.

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