We have an opening for a highly motivated Postdoctoral Researcher to conduct research in computational structural biology to develop methods for predicting protein-protein interactions in host-pathogen systems using deep learning (DL). Current structure prediction tools such as RoseTtaFold3, AlphaFold3, and ESMFold2 excel at monomeric structure prediction and predicting known protein complexes. However, these tools struggle to identify which proteins interact and which do not. You will work to develop specific models that can discriminate between interacting and non-interacting proteins. You will be an integral member of an interdisciplinary, cross-institution team working with computational biologists, and experimental biologists. You will leverage computational tools and work to develop new DL-based approaches and tools to predict binary interactions, specificity, and structure. You will also work closely with an existing team of computational biologists to understand current capabilities and jointly develop a vision for development of next generation protein design models and tools. You will present your work regularly and publish your research and findings, which includes occasional travel. This position is in the Computational Engineering Division (CED), within the Engineering Directorate. Depending on your assignment, this position may offer a hybrid schedule, blending in-person and virtual presence. You may have the flexibility to work from home one or more days per week.
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Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree