The Busch and Pereira groups are seeking researchers interested in working at the intersection of computer vision and biology, with an emphasis on the development and application of deep learning-based models for plant root phenotyping. The Busch lab aims to understand which genes, genetic networks, and molecular processes determine root growth and its responses to the environment; and the Pereira lab aims to engineer tools for capturing and modeling complex biological systems through the use of deep learning and computer vision. This research is directly leveraged in the pioneering CRoPS project that aims to develop crop plants that sequester more carbon via extensive root systems containing recalcitrant carbon polymers to fight climate change. For this we are working to identify genetic and molecular mechanisms, as well as genetic engineering strategies to enhance the carbon sequestration capacity in plants. We are currently seeking a highly motivated and independent postdoctoral fellow interested in learning, applying and developing new deep learning methods for tasks including image segmentation, landmark detection and 3D reconstruction. In collaboration with experimentalists, this work will help in identifying, studying and engineering the mechanisms controlling root traits in model and crop plant species. In addition to the primary application domain, we are also interested in furthering the state-of-the-art in computer vision and deep learning, and expect that most of the models developed as part of this research program will be broadly applicable to other areas.
The ideal candidate will be coming from a computationally-intensive background and have demonstrated experience with computer vision and deep learning. They will be driven to learn new methods, computational frameworks, and eager to collaborate with experimental researchers to uncover new and impactful biology and to apply this knowledge to the larger problem of climate change. The candidate will be working as part of a committed and diverse team, and will be eager to make contributions to the entire scientific workflow, from experimental design and implementation, to the development of new analytical tools and the software engineering necessary to deploy it. They will be working with both plant biologists as well as computational scientists and scientific software engineers.
Ph.D. in Computer Science, Data Science, Machine Learning, Computational Biology, Physics, Electrical Engineering, Mathematics, Statistics, Biological Sciences, Agricultural or Plant Biology or related fields, provided they have a strong computational background.
Applicants should submit a current resume, a link to their GitHub with code from previous work, a list of their pre-prints and publications, names of three references, and an indication of how their expertise and academic accomplishments make them a good fit for the position.
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