<!--anchor--> <!--anchor--> Postdoctoral Fellow - Computational Biology, Li & Moussion Lab <!--anchor-->

The Li (gRED Computational Sciences) and Moussion (Cancer Immunology)Labs at Genentech are looking for an exceptional Postdoctoral Fellow to lead the computational efforts for better understanding the crosstalk between tumor cells and the tumor microenvironment (especially immune cells) in cancer treatment. The projects will be in close collaboration with the Riggi lab in Cellular and Tissue Genomics. 

The successful applicant will have the opportunity to work on novel datasets produced from precious model organisms (e.g. mouse) and human samples by cutting-edge high throughput technologies, such as single-cell RNA-Seq, single-cell multiomics, spatial omics, and high content imaging. By working closely with wet-lab scientists in Moussion and Riggi labs, the applicant will contribute to identifying new therapeutic strategies to modulate the crosstalk between tumor and immune cells and improve response to therapy. There might also be opportunities to develop novel algorithms and analytic strategies inspired by the data generated from the project. This position will be co-mentored by Drs. Li and Moussion. 

This position will have an opportunity to collaborate across multiple groups whose expertise spans different scientific disciplines and approaches, including cancer immunology, molecular/discovery oncology, biochemistry, computational biology, and AI/ML. Scientific insights resulting from this research are expected to be presented at scientific conferences and published in high-impact journals. Opportunities for the clinical translation of new discoveries are also in place.

• Collaborate with wet-lab scientists from Moussion and Riggi labs to advance projects together.
• Publish high quality papers based on biological advances discovered in this work.
• Create scientifically rigorous visualizations, communications and presentations of results.
• Contribute to open-source software development by summarizing novel analysis strategies learned from this work. 

• Experienced with analyzing cancer-related omics datasets from mouse and human samples.
• Experienced with single-cell omics data analysis (e.g. single-cell RNA-seq, Perturb-seq, 10x multiome).
• Demonstrated proficiency with the Python data analysis ecosystem.
• Demonstrated ability to effectively communicate about complex bioinformatics problems to peers, users and leadership.
• Independent, highly motivated, and highly collaborative with the ability to work together with multi-disciplinary teams of computational scientists and biologists
• Familiarity with spatial transcriptomics / proteomics analysis is a plus
• Familiarity with high-content image analysis is a plus.
• Familiarity with deep learning frameworks such as PyTorch and TensorFlow is a plus.
• You are enthusiastic about working in a scientific environment, especially one that is related to drug discovery and development.
• You are a quick learner, are curious about new areas and the opportunity to build expertise, and courageously and creatively take initiative to see your ideas implemented.
• You are able to perform at a high level in a fast changing and demanding environment.

Li Lab:

Dr. Li is a Principal Scientist in gRED Computational Sciences. His lab focuses on single-cell multiomics and spatial omics data analysis and tool development, with applications to oncology. Dr. Li is a leading expert in analysis and tool development for large-scale high-throughput genomics data. He developed RSEM, a gold standard tool for transcript quantification from bulk RNA-Seq data. The RSEM papers have been cited 15,582 times (Google Scholar) and the tool itself has been used by big consortia projects such as ENCODE (ENCyclopedia Of DNA Elements), TCGA (The Cancer Genome Atlas), GTEx (Genotype-Tissue Expression) and TOPMed (Trans-Omics for Precision Medicine). His more recent work, Cumulus, is the first comprehensive cloud-based analysis engine for single-cell RNA-seq and was published in Nature Methods (impact factor 47.99). Prior to joining Genentech, Dr. Li was Assistant Professor of Medicine at Harvard Medical School, Assistant Investigator of Massachusetts General Hospital, and Associate Member of the Broad Institute of MIT and Harvard. He did his Ph.D. in Computer Science from the University of Wisconsin-Madison and completed two postdoctoral training with Lior Pactor at UC Berkeley and Aviv Regev at the Broad Institute respectively. For more details, please visit Li Lab website athttps://lilab-bcb.github.io/ . 

Moussion Lab:

The Moussion lab studies mechanisms of resistance to Immunotherapy in solid tumors by leveraging a novel in vivo high content imaging platform named STAMP (Ortiz-Muñoz et al, Nature, 2023 ). Dr. Moussion received her PhD from the University of Toulouse (France) where she studied the role of Dendritic cells in the control of lymphocyte migration from blood to lymph node at IPBS. She then moved to IST Austria in Vienna as a Postdoctoral fellow to study the mechanisms of Dendritic cells migration from peripheral tissue to the draining lymph node through lymphatic circulation. In 2016, Dr. Moussion joined Genentech as a group leader in the Cancer Immunology Department. Her group bridges basic science and drug discovery to target novel cellular and molecular mechanisms that limit the recruitment of leukocytes in the context of an anti-tumor Immune response. By combining innovative high-throughput in vivo imaging technologies with genetic and pharmacological tools, her group pursues discoveries of novel biological pathways from mouse models to human pathology to improve the outcome of Immunotherapies in the treatment of solid tumors. 

Relocation benefits are available for this job posting.

The expected salary range for this position based on the primary location of California is $64,800 and $120,300.  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

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