The Position A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Genentech. Are you a highly passionate computational biologist looking to make a significant impact in cancer immunology? Join Genentech's Computational Sciences Center of Excellence as a Postdoctoral Fellow, where you'll conduct cutting-edge research at the intersection of computational biology and oncology. This position offers a unique opportunity to be jointly mentored by experts in our Computational Biology and Medicine department and our Oncology Research organization. The Role: Primarily focused on tumor antigens, T cell receptor (TCR) and antigen interactions and their crucial role in anti-cancer immune responses. You'll leverage your strong background in computational biology, machine learning, and structural biology to tackle challenging scientific questions. Your responsibilities will include, but are not limited to: Multi-omics analysis of bulk and single-cell sequencing data. Developing deep learning models to predict TCR-peptide/MHC (pMHC) interactions. Utilizing structural computational biology techniques to characterize and model TCR-pMHC interactions. Designing experiments to test and validate hypotheses. This is a fantastic opportunity to contribute to world-class science in a leading biotechnology company. Who you are: Candidates must have a PhD in Computational Biology or Computational Science. Alternatively, a PhD in Molecular Biology or Immunology with computational biology as the primary focus. Impactful researcher with one or more first-author publications in high quality journals. Strong experience in application-based computational biology, including single-cell sequencing techniques. Strong experience in deep learning and AI. Structural biology experience is a strong plus point. Pragmatic and focused researcher, driven to solve complex scientific problems independently and in a collaborative environment. Efficient oral and written communication skills are required. For information about the Jhunjhunwala Lab at Genentech, please go to: Relevant publications: Thrift, W. J. et al. Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity. Briefings in Bioinformatics 25 (2024). Thrift, W. J. et al. HLApollo: A superior transformer model for pan-allelic peptide-MHC-I presentation prediction, with diverse negative coverage, deconvolution and protein language features. bioRxiv (2022) doi:10.1101/2022.12.08.519673. Jhunjhunwala, S., Hammer, C. & Delamarre, L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat Rev Cancer 1–15 (2021) doi:10.1038/s41568-021-00339-z. Chen, J. et al. In silico tools for accurate HLA and KIR inference from clinical sequencing data empower immunogenetics on individual-patient and population scales. Brief Bioinform bbaa223- (2020) doi:10.1093/bib/bbaa223. Capietto, A.-H. et al. Mutation position is an important determinant for predicting cancer neoantigens. J Exp Medicine 217, (2020). Kong, Y. et al. Transposable element expression in tumors is associated with immune infiltration and increased antigenicity. Nat Commun 10, 5228 (2019). Yadav, M. et al. Predicting immunogenic tumour mutations by combining mass spectrometry and exome sequencing. Nature 515, 572–576 (2014). Postdoc Program The Genentech Postdoc Program provides access to world-class seminars, professional development workshops, and networking opportunities. The expected salary range for this position based on the primary location of California is $110,000 to $120,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for the benefits detailed at the link provided below. Benefits Relocation benefits are available for this job posting.
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