An computational postdoctoral position in deep learning is available in Dr. Haiyuan Yu’s group at Cornell University. Dr. Yu’s group performs research in the broad area of Network Systems Biology with both high-throughput experimental (see Vo et al., Cell 2016) and integrative computational (see Wang et al., Nature Biotechnology 2012) methodologies, aiming to understand gene functions and their relationships within complex molecular networks (protein-protein interactome networks in particular) and how perturbations to such systems may lead to various human diseases. We are especially interested in developing machine learning algorithms to predict protein-protein interaction interfaces (see Meyer et al., Nature Methods, 2018) or causal mutations for various disorders (see Chen et al., Nature Genetics, 2018). Representative publications from the lab can be found below. Please visit the lab website (www.yulab.org) for more information and a full list of publications.
We are looking for motivated post-doctoral researchers to develop deep learning algorithms: 1) to integrate 3D protein structures and many other sequence, evolutionary, genomic/proteomic feature to predict protein-protein interface residues and impact of missense mutations on protein stability and interactions; 2) to integrate various functional genomics datasets and 3D chromatin structures to predict active transcription regulatory elements (enhancers in particular) and their target genes, and impact of noncoding mutations on gene expression. The ideal candidate should have a strong publication record and extensive experience in computational biology. Experience with machine learning algorithms (especially deep learning) and familiarity with various types of large genomics/proteomics datasets are strongly preferred. Experience in network biology or systems biology is not required. The starting date is flexible and can be as early as December 2018. Applications will be accepted until the position is filled. Competitive salary commensurate with experience and skills, as well as a generous benefits package will be offered. Interested applicants should send a PDF with CV, a brief description of future research interests (optional), and contact information for three references to Dr. Haiyuan Yu (email@example.com). Please include “Deep Learning Postdoc 2018” in the subject line. Informal inquiries are welcome.
- An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders, Nature Genetics, 2018
- Interactome INSIDER: a structural interactome browser for genomic studies, Nature Methods, 2018
- A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human, Cell, 2016.
- Three-dimensional reconstruction of protein networks provides insight into human genetic disease, Nature Biotechnology, 2012.
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