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methods involving molecular surface display and deep sequencing to study force-dependent behavior in protein systems. These datasets will, in turn, be used to train machine learning models capable
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and adoptive transfer, spectral flow cytometry, cell sorting, imaging, transcriptomics, single-cell RNA sequencing, spatial transcriptomics, and functional neuro-immune assays. Your profile We
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MR pulse sequence design and image reconstruction approaches, excellent image quality is achievable. Moreover, given the lower static field, options to stacked and concurrent imaging of electric and
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design follow-up wet lab experiments with colleagues. Integrate scRNAseq datasets with other omics information, including spatial transcriptomics, ATAC sequencing, metabolomics and/or multiplex imaging
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of analytical processes and computational infrastructures. • Coordinate interfaces between sequencing platforms, analytical environments, data repositories and data-sharing infrastructures. • Support the
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/transcriptomics team that develops and implements latest next generation sequencing (NGS) methods and strategies and provides services and training to the research groups accessing the center. The position offers
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on single-cell and spatial genomics data, from sequencing and imaging, and apply them to human developmental and disease biology. Possible research directions include: ● Generative models of cell–cell
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, shared autonomy, multimodal intent inference, and reasoning over sequences of human and robot actions. Job description Develop signal-processing and machine-learning methods for multichannel EMG, including
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drives innovation in next-generation sequencing, single-cell and spatial genomics, and emerging multi-omics approaches. To strengthen our R&D and user support activities, we are seeking a highly motivated
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Your position The overall goal of the lab is to understand the cause of multiple sclerosis, focusing on the interactions between autoreactive B cells, the lymphotropic virus EBV, and the cellular