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apply cutting-edge machine learning algorithms, with focus on foundation models and LLMs/agents, to analyze complex biological data. This data includes gsingle cell genomics profiles, spatial data, and
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the molecular pathways of disease and to establish the mechanism of action of our therapeutics. The successful candidate will work with our team to analyze multi-level biomarker data generated from pre-clinical
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technologies (single cell, CRISPR, spatial) and microscopy are highly desired. Expertise in spatial-omic (Xenium) data analyses and interpretation is highly desired. Experience with new technology development is
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) organization which uses data, by developing best in class computational methods, and applying them to the most relevant scientific problems across all stages of the pipeline. This position is based within
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perturbation technology, AI/ML model development, or advanced molecular biology Experience in single-cell omics, spatial transcriptomics, and/or high-content imaging data analysis Demonstrated record
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screens through analysis of imaging (e.g CellPainting) and spatial-omics data Leverage data to build predictive models of cellular response which can be validated experimentally Who You Are: Ph.D. in
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: Experience with scRNA-seq and flow cytometry for immune cell profiling. Genome Engineering: Background in using CRISPR for creating disease-relevant cell models. - Data Analysis: Familiarity with
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author paper published or accepted in a well-recognized peer-reviewed journal More information about Man-Wah Tan’s Lab and recent publications: https://www.gene.com/scientists/our-scientists/man-wah-tan
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teams. The Opportunity: Opportunity to work closely with computational colleagues to analyze, evaluate, and perform integrative computational analyses. Leverage multimodal high dimensional data to explore
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will be part of the Genentech post-doctoral training program that provides an outstanding environment and the opportunity to pursue fundamental basic science questions in an environment focused