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Field
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leading role in research development, research articles, and proposal writing in transcriptome and metabolomics research. The successful candidate will contribute significantly to expanding the laboratory's
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biology, microbiology, genomics, transcriptomics, and proteomics techniques. - Collaborate with in-laboratory microbiologists, virologists, parasitologists, vector biologists, and bioinformatics
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Equation, Stochastic simulation algorithms, and approximation methods. ● Experience with single-cell or spatial transcriptomic data analysis. ● Familiarity with machine learning and deep learning
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) biology and osteoarthritis - Mouse genetic models and lineage tracing - Single-cell RNA sequencing and spatial transcriptomics - Skeletal mechanobiology and functional loading models - Gene therapy and
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for a creative post-doctoral scientist with strong background in soil microbiome, multi-omics [metagenomics/transcriptomics/metabolomics], as well as bioinformatics. Responsibilities: This researcher will
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consortium (academia, clinics, and industry). Interdisciplinary skillset: from CRISPR/iPSC gene editing and single-cell transcriptomics to microfluidics, proteomics, and translational validation in human
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, relative to career stage. Develop novel statistical methods for high dimensional or complex biological data, including genomics or transcriptomics. Demonstrate success securing competitive research funding
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single-nucleus RNA sequencing (scRNA-seq/snRNA-seq) is an advantage. Knowledge of data analysis methods, including statistical software and transcriptomic analysis pipelines is an advantage. Excellent
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project focuses on developing probabilistic latent-variable methods for large and structured biological data, with applications in genomics, spatial transcriptomics, and fluorescence imaging. High
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in size and scientific scope. The position will also support additional collaborative projects involving single-cell RNA sequencing, spatial transcriptomics, and other emerging multi-omic technologies