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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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analyses. Machine learning for biological data (e.g., protein language models, transformers, generative models) and interest in building interpretable tools for experimental colleagues. Qualifications PhD
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staff with excellent interdisciplinary knowledge and specialized, state-of-the-art expertise in software engineering, computer visualization, and data science. As a faculty member, the applicant is
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fellow to join our translational research program in macrophage biology/immunology. Our team takes a systems approach—integrating multi-omics, network science, machine learning, and comprehensive in vitro
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methodology. Applying AI and machine learning (ML) tools (including Python, R, and possibly other languages) to test and evaluate biomedical hypotheses. Developing benchmarks and working together with staff
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computational pipelines for multiplex imaging, spatial transcriptomics, single cell RNAseq, and multi-omics data integration. Lead graph-based network and machine learning analyses of tumor immune
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Lab at Princeton University aims to recruit a postdoctoral fellow or more senior research position to work on projects related to the development of AI/machine learning approaches for chemical and
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or Full Professor with an MD, PhD, DVM or equivalent degree. The preferred areas of Virology research include: i) basic and molecular virology, ii) Virus-host interactions and iii) Molecular mechanisms
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has a passion for continuing to push the boundaries of our understanding. Candidates who demonstrate responsibility, initiative, and a strong drive to learn and succeed in a collaborative environment