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for ambitious scientists seeking to transition to independent research leadership while contributing to cutting-edge, interdisciplinary projects at the intersection of AI Virtual Cell modeling, Epigenetics, and
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projects at the intersection of AI Virtual Cell modeling, Epigenetics, and Stem Cell biology. Research Focus for This Position We are particularly interested in candidates who can integrate AI-driven virtual
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. Applicants should have a Ph.D. in Biological/Biomedical Sciences, Microbiology, Bioengineering, or a related field, with experience in cell culture, molecular and cell biology, and animal models. Skills in
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people’s lives taking research a step beyond the lab. Research projects we are looking to pursue are in the areas of extracellular vesicles & cell therapy, immunotherapy, nucleic acids therapeutics for brain
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bioinformatics, deep learning and/or biomedical image and clinical data analysis (e.g. Linux, R programming) is essential. The appointees will need to perform data analysis of single cell RNA-sequencing
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, or related fields. Those with research experience in exosome, cell and animal handling are preferred. Responsibilities: Conduct literature review and experiments for relevant research projects; Supervise
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: Applicants should possess a relevant PhD degree with a solid track record in research. Candidates with strong background and research experience in molecular and cell biology, biochemistry, or genetics
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will be given to candidates with research experience in cancer bioinformatics, cancer biology and/or cancer immunology; as well as cell/organoid culture, cellular/molecular biology, flow cytometry
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exceptional experimental, writing, and communication abilities. Preference will be given to candidates with research experience in cancer bioinformatics, cancer biology and/or cancer immunology; as well as cell
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bioinformatics, deep learning and/or biomedical image and clinical data analysis (e.g. Linux, R programming) is essential. The appointees will need to perform data analysis of single cell RNA-sequencing