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sequencing, and with computer scientists at KTH in Stockholm, focused on developing scalable probabilistic machine learning techniques for online phylogenomic analysis and placement of DNA barcodes. You will
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develop analysis pipelines to analyze high dimensional spatial and single-cell data of cancer and immune tissue from patients and pre-clinical studies and should have a strong background in both
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or other forms of appointment/assignment relevant to the subject area. Required skills: Strong background in aquatic ecosystem science Proficiency in GIS and analysis of long-term environmental data
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to be awarded, a PhD degree or equivalent in biochemistry, cell or molecular biology, genetics or a related discipline. We are seeking highly motivated applicants with practical experience in
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combining imaging techniques and data analysis to provide a more integrated picture of life processes in the context of health and disease. To be a postdoc fellow at the AMBER programme you will get
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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processes. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex biological systems
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experimental and theoretical approaches. Requirements: Well-documented expertise in any of the following areas: next-generation sequencing data analysis (singe cell and spatial sequencing). PhD in
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microorganisms, and developing of spectral collection and analysis protocols that will allow this biochemical data to be effectively used to support optical microscopy-based deep-learning algorithms for species
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data and clinical information. Applicants must hold (or be close to completing) a PhD in a relevant field and have expertise in modern computer vision and AI research. Experience with biomedical data