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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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streamline and accelerate the development of the projects. Share research through scientific publications, national and international conferences, and internal presentations. Who You Are: PhD graduate in
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computational colleagues to build, train, and evaluate cutting edge AI models using large proprietary oncology datasets Leverage multimodal high dimensional data to investigate relationship between heterogeneous
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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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. Scientific insights resulting from this research are expected to be presented at scientific conferences and published in high-impact journals. The Opportunity: Generate new methods for large-scale spatial
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applicant will have an opportunity to work closely with a diverse scientific team that includes microbiologists, immunologists, cell biologists and bioinformaticians. Who You Are: Recent PhD in the field
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statistical genetics Proven experience with applying standard pipelines for the analysis of large-scale genetic data sets Demonstrated proficiency with R or Python Demonstrated ability to effectively
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presented at external conferences. Who you are: PhD in structural biology, biochemistry, or a related field with one or more first-author publications in leading peer-reviewed journals. A strong background in
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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