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sequencing data will be mapped to chromosome-level reference genomes provided by our partners at Project Psyche (https://www.projectpsyche.org/ ). Access to continental-scale field samples and standardized
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mindset. You have good statistical skills and experience with analyzing metabolomic and metagenomic sequencing data. You like to work in diverse settings as you have to communicate closely with a diverse
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sequencing data. You like to work in diverse settings as you have to communicate closely with a diverse team (i.e. microbiologists, data analyst, cell biologists, immunologist, engineers). You are a team
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 16 days ago
architecture of important crop traits like grain yield heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck
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, the PhD student will have at his/her disposition a set more than 150 high quality genomes (assembled from long-read sequencing and polished with short-reads), more than 100 of P. destructans and more
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sequencing, and mass spectrometry. With multidisciplinary research teams and a dynamic setting, the IBMC offers a stimulating environment conducive to scientific innovation. The IBMC is also affiliated with
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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assays and RNA sequencing in iNeurons. Using advanced techniques such as automated live-cell imaging, electrophysiology and biomarker analyses, effective AONs will be selected. Ultimately, the goal is to
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the Ruijtenberg lab, we study these mechanisms using a combination of genome-wide sequencing and single-molecule imaging approaches, aiming to understand how cells determine their fate and function. Your job In
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image sequences. As a benchmark, end-to-end deep learning models will be developed using raw image data. In parallel, shallow learning models (e.g., Gaussian processes) will be explored based on insights