This NSF funded position is a collaborative project between five institutions (University of South Dakota, Seattle Children’s, Virginia Tech, Drexel, and Tulane) to develop next-generation biology-guided convolution neural nets to enable machine discovery of organismal traits using existing 2D and 3D annotated imagery from aggregate specimen archives (such as MorphoSource, or iDigBio), as well as existing information from developmental and anatomical ontologies and phylogenies. Post-docs in the program will have a chance to cross-train in different fields of phylogenetics, vertebrate morphology (ichthyology in particular), data-science and machine learning through rotations in participating sites. For the project at Dr. Maga’s lab, we are seeking trainees that either have PhDs in computer vision (or related) research and interested in applying their research in organismal biology context, or biologists with a strong computational background interested in learning image analysis. This is a two-year position, with an option to renew based on performance and pending project renewal. Expected start date is December 1st 2019 (or sooner).
Interested candidates should submit their CVs and contact information for (up to three) referees to Dr. Murat Maga ([email protected]), along with a cover letter explaining which specific position they are applying for. Inquiries regarding positions can also be directed to the same email address. Applications received by September 1st will be given priority. For both positions a small relocation allowance is negotiable.
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