Computational Postdoc: predicting how human stem cells differentiate using single-cell microscopy, image analysis and AI
Organization:
University of Bristol
Location: Available from:
September 1, 2019
to
August 31, 2021 Making personalised stem cell therapeutics a reality will require that we understand how to predictively engineer in vitro replacement cells and tissues robustly and in a tumour-free manner, on a person-by-person basis. Towards that goal, the Carazo Salas group is establishing innovative experimental and computational tools and pipelines combined with human pluripotent stem cell technologies (hESC, hiPSC), to elucidate the quantitative & mechanistic basis of efficiency, specificity & tumourigenic potential in human pluripotent stem cell differentiation and identify ways to improve personalised tissue engineering. We have recently established large-scale, multiday, multicolour time-lapse microscopy pipelines allowing us to follow at single-cell level how ‘live’ human stem cells proliferate and differentiate over time. This means we routinely image thousands of ‘live’ human stem cells in multiple epifluorescence microscopy channels every 10 minutes through multiple days, generating millions of single-cell data points from which we want to derive predictive information about cell fate. We are looking to hire a highly motivated and talented computational postdoc with prior expertise in quantitative image analysis (particularly in cell segmentation and tracking from multi-channel time-lapse fluorescence microscopy images) and machine learning (particularly novel approaches like CNNs, GANs) to help us extract from those images information enabling us to understand and predict why each cell makes the fate decision it makes. The selected computational scientist will work closely with experimentalists and as part of a larger collaboration with groups in Switzerland and the USA. Applicants should hold a PhD in Computational Image Processing, Computer Vision, Machine Learning, or a related subject, have an excellent track record and extensive experience with computational image analysis and/or machine learning, and be excited to work in an interdisciplinary environment. Experience with timelapse microscopy imaging of cells or high-throughput/high-content microscopy is a plus. For enquiries, please contact Rafael E. Carazo Salas at rc16805@bristol.ac.uk.
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Organization:
University of Bristol
Location: Available from:
September 1, 2019
to
August 31, 2021 Making personalised stem cell therapeutics a reality will require that we understand how to predictively engineer in vitro replacement cells and tissues robustly and in a tumour-free manner, on a person-by-person basis. Towards that goal, the Carazo Salas group is establishing innovative experimental and computational tools and pipelines combined with human pluripotent stem cell technologies (hESC, hiPSC), to elucidate the quantitative & mechanistic basis of efficiency, specificity & tumourigenic potential in human pluripotent stem cell differentiation and identify ways to improve personalised tissue engineering. We have recently established large-scale, multiday, multicolour time-lapse microscopy pipelines allowing us to follow at single-cell level how ‘live’ human stem cells proliferate and differentiate over time. This means we routinely image thousands of ‘live’ human stem cells in multiple epifluorescence microscopy channels every 10 minutes through multiple days, generating millions of single-cell data points from which we want to derive predictive information about cell fate. We are looking to hire a highly motivated and talented computational postdoc with prior expertise in quantitative image analysis (particularly in cell segmentation and tracking from multi-channel time-lapse fluorescence microscopy images) and machine learning (particularly novel approaches like CNNs, GANs) to help us extract from those images information enabling us to understand and predict why each cell makes the fate decision it makes. The selected computational scientist will work closely with experimentalists and as part of a larger collaboration with groups in Switzerland and the USA. Applicants should hold a PhD in Computational Image Processing, Computer Vision, Machine Learning, or a related subject, have an excellent track record and extensive experience with computational image analysis and/or machine learning, and be excited to work in an interdisciplinary environment. Experience with timelapse microscopy imaging of cells or high-throughput/high-content microscopy is a plus. For enquiries, please contact Rafael E. Carazo Salas at rc16805@bristol.ac.uk.
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