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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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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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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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collaboration with the group led by Asst. Prof. Gina Panopoulou. This includes code development, analysis and interpretation of data, writing manuscripts for publication and presenting work at relevant
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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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-related activities at the department (20%), including supervision of MSc and PhD students Possibilities for international collaboration and research exchanges Access to pedagogical training and development
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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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communication skills in English. Strong skills in programming and data analysis. Track record of scientific publications in international peer-reviewed journals. Meritorious qualifications include: Experience
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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