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Pacific Institute for the Mathematical Sciences | Northern British Columbia Fort Nelson, British Columbia | Canada | 6 days ago
years, depending on start date and available funding. The position will be hosted at Simon Fraser University, focusing on the mathematical analysis and development of novel structure-preserving
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procedures used in spatial data analysis and vegetation dynamics modeling is required. The candidate should have extensive practical experience in the use of R and GIS. Demonstrated skills of Bayesian modeling
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required Experience analyzing wearable device data including accelerometer and GPS data, as well as experience with spatial data analysis Demonstrated publication record in high quality peer-reviewed
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Job Description We invite applications from highly motivated and talented researchers for a 2-year postdoctoral position in advanced skin models, cellular immunology, and spatial transcriptomics
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establish routine cell surface proteome analysis using subcellular fractionation techniques, such as density gradient centrifugation and surface biotinylation followed by enrichment, to explore the surface
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establish routine cell surface proteome analysis using subcellular fractionation techniques, such as density gradient centrifugation and surface biotinylation followed by enrichment, to explore the surface
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techniques including single cell RNA sequencing and spatial transcriptomics. The individual will have the opportunity to work on their professional development and specific skills needed for their professional
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- Proficiency in R and/or Python programming - An interest in interdisciplinary research spanning ecology, remote sensing, spatial analysis and mathematical ecology - Experience in writing and publishing peer
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ED data acquisition, analysis and use of in situ liquid and electrochemical TEM experiments, under the supervision of Professor Joke Hadermann. EMAT is one of the leading electron microscopy centers
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encourage all qualified candidates, regardless of background, to apply! Job description The candidate will work on large-scale data analysis of cancer transcriptomics data (bulk, single cell and spatial), and