10 model-driven-development "Integreat Norwegian Centre for Knowledge driven Machine Learning" Postdoctoral positions at SciLifeLab
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candidate will join the Scientific Machine Learning group at TDB and SciLifeLab. The group develops theory, methods and software for data-driven science, with a current focus on uncertainty quantification
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samples, lack of training data and sample variability. In this project we aim to develop AI/ML workflows for improved quantitative analysis of LNPs. Your responsibilities will include optimisation of data
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expertise. Are scientifically curious, independently driven, and motivated by biologically meaningful modelling problems. Have good teaching abilities. Have awareness of diversity and equal opportunity issues
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constellation of SciLifeLab researchers and infrastructure units. This position is embedded in Avlant Nilsson’s research group at Karolinska Institutet and SciLifeLab. Our lab develops deep learning models
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research environments in Computational Science, the research and education has a unique breadth, with large activities in areas such as numerical analysis, mathematical modelling, development and analysis
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and variation. The successful candidate will develop innovative methods and models to advance our understanding of genome evolution and variation. The position is based in the Computational Genomics
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SciLifeLab. The successful candidate will work closely with Avlant Nilsson cancer cell modeling lab and Wei Ouyang’s AICell lab who together will develop the first foundation cell model based on HPA and other
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platform to identify and optimize therapeutic candidates. Our group specializes in developing technologies to assess the multicellular environment within three-dimensional microtumor models. The project
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an outstanding and ambitious postdoctoral researcher in computational biology to pioneer understanding and modeling of tissue architecture using single-cell and spatial transcriptomics data. The focus will be
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model organisms, their application in conifers remains limited due to challenges associated with tissue structure, nuclei isolation and sensitivity to inhibitory compounds. This project aims to develop