The Noble lab has three openings for postdoctoral fellows to work on machine learning projects related to genomics and proteomics. An ideal candidate will be motivated by the desire to develop and apply novel machine learning techniques. Familiarity with genomic or proteomic data is not required, but candidates must be prepared to become familiar with these data and the generative processes that underlie them. The three projects are (1) machine learning models for integrating multi-omic data, as part of the NIH Multi-Omics for Health and Disease Consortium, (2) deep learning models for mass spectrometry proteomics, and (3) machine learning analysis of chromatin conformation data.
Candidates must have a PhD or equivalent.
Candidates must have strong computational skills.
Expertise in machine learning is desirable.
Expertise in relevant areas of cell biology, molecular biology, bioinformatics, and stem cell biology is not required, though helpful.
Applicants are also expected to be intellectually curious, independent, proactive, team-oriented, and with a strong work ethic. Strong verbal and written skills in English are critical.
Email CV and cover letter to william-noble@uw.edu.