The laboratory of Professor X. Shirley Liu at Dana-Farber Cancer Institute and Harvard T.H.Chan School of Public Health invites applicants for the following positions. The research in the laboratory focuses on designing bioinformatics algorithms and integrative genomics approaches to model gene regulation, find novel drug targets and combinations, and predict response to targeted and immunotherapies in cancer.
A representative list of recent projects and publications (with Liu Lab as major contributors) include:
- Algorithm development: (Zhang et al, Genome Biol 2008; Li et al, Genome Biol 2015; Wang et al, Genome Res 2016; Li et al, Nat Genet 2017; Jiang et al, accepted to Cell Systems)
- Data integration: (Jiang et al, PNAS 2015; Jiang et al, Nat Genet 2015; Jiang et al, Genome Biol 2015; Zang et al, Nat Comm 2016 ; Du et al, Nat Comm 2016)
- Cancer Epigenetics: (He et al, Nat Genet 2010; Xu et al, Science 2012; He et al, Nat Meth 2014; Liu et al, PNAS 2017; Mei et al, Cancer Res 2017 )
- CRISPR screens: ( Li et al, Genome Biol 2014; Xu et al, Genome Res 2015; Zhu et al, Nat Biotech 2016; Fei et al, PNAS 2017 )
- Cancer Immunology: ( Li et al, Genome Biol 2016 ; Li et al, Nat Genet 2016 ; Liu & Mardis, Cell 2017 ; Li et al, Cancer Res 2017 ; Pan et al, Science 2017 )
Dana-Farber Cancer Institute (DFCI) was recently awarded a five-year grant from the National Cancer Institute (NCI) as part of the Cancer Moonshot Project to build a Cancer Immunologic Data Commons (CIDC). NCI plans to conduct comprehensive profiling of tumor, blood, and fecal samples from NCI-sponsored immuno-oncology trials at four Cancer Immune Monitoring and Analysis Centers (CIMACs: Stanford, MD Anderson, Mt Sinai, and DFCI). The CIDC is responsible for building the database infrastructure, bioinformatics pipelines, computational algorithms for the resulting profiling data and related clinical data, enabling integrated analysis across trials and sharing of all data by the larger immuno-oncology research community. The goal of the project is to enable systematic incorporation of biomarker studies in NCI-supported early immunotherapy clinical trials to better understand and predict which patients respond to certain immunotherapy treatments.
The CIDC project currently seeks a Lead Computational Biologist for the CIDC. The Lead Computational Biologist will work closely with laboratories of DFCI, CIMAC and CIDC investigators, especially laboratories of Drs. X. Shirley Liu, Ethan Cerami, Catherine Wu, and Steve Hodi on various bioinformatics aspects of immune-profiling tools and analyses. We are looking for exceptional candidates with strong computational biology, immunogenomics, and communication skills.
Computational biology postdoc position at the X. Shirley Liu Lab
Ideal applicant should have:
- A PhD degree in related field (bioinformatics, physics, statistics, engineering, etc) received in the last 3 years
- Strong quantitative background (machine learning, Bayesian inference, etc.) or computational genomics experiences (high throughput sequence analysis, etc.)
- Strong programming skills: ((Python or C or C++ or Java) & R)
- At least two first authored English papers (or three if co-first authors) with submitted, accepted or published status in journals
- Good spoken and written communication skills in English. Interested applicants should submit CV, a letter of interest with a one-page proposal for a project to be conducted in the Liu Lab, and contact of three references to Shirley Liu with subject line “Postdoctoral application”.
Data scientist in translational cancer research
The Shirley Liu Lab is recruiting a data scientist to develop machine learning algorithms and pipelines, build databases and interactive web servers, and conduct high throughput data analyses and integration to model tumor immunology and understand cancer patient response to immunotherapy. We recently received funding for a Cancer Moonshot Project by the National Cancer Institute to build a Cancer Immunologic Data Commons (CIDC). NCI plans to conduct comprehensive profiling of clinical samples from immuno-oncology trials. The data scientist will work on various computational aspects of immune-profiling algorithms and analyses, with the goal of identifying biomarkers for optimizing immunotherapy strategies in cancer.