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, genetic models of disease, and advanced therapeutics. This permanent full-time faculty position will be housed at the SickKids Research Institute, located in the heart of downtown Toronto. SickKids is fully
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hold a PhD or MSc in Systems Biology, Biostatistics, Physics, Mathematics or a related subject. Experience with mathematical modelling of biological systems and the ability to effectively use statistical
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. The three posts are: Research Associate in Computational Chemistry. You will focus on the development of computational screening methods and property prediction models using both data-driven and simulation
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the pharmacometric research group, ensuring the successful delivery of its projects and research outputs. This includes developing PK/PD models using both clinical and pre-clinical data, supporting clinical trial
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opportunities suited to green competitive advantage of key countries in Latin America, especially Mexico. The Sr. Research Data Analyst will build complex data models and conduct complex data analysis in support
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modelling. Furthermore, better knowledge of these properties helps refining the interpretation of data obtained from both active and passive remote sensing. PhD Topic This PhD project aims to quantitatively
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on the SAFT-g Mie equation of state. These models are applied to predict the behaviour of complex mixtures, with particular relevance to pharmaceutical systems. This position is part of the Mediforge
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to green competitive advantage of key countries in Latin America, especially Mexico. The Sr. Research Data Analyst will build complex data models and conduct complex data analysis in support of research
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will be responsible for: Liaising with other ECORES-WIND project partners, both in academia and the industry, and to enable quick and efficient sharing of information and modelling results within
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allocation proposals, conducting machine learning workflows, and developing complete models. Example applications include microscopy image data, cryo-electron microscopy, structural prediction and dynamic