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tasks for the position The candidate will pursue research on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case of molecular data in cancer
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interested in connecting spatial and spectral information to understand complex materials systems at the molecular level with machine learning. PhD Student A will work with tumour sections to develop multiple
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coating, iii) investigation of system design from small-scale to potentially pilot scale, and iv) application to micropollutant removal. Modelling aspects are open to exploration at molecular and process
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The molecular biosciences are undergoing a major paradigm shift – away from analysing individual genes and proteins to studying large molecular machines and cellular pathways, with the ultimate goal
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perception, decentralization and mission execution. The RAI team has a strong European participation in multiple R&D&I projects, while RAI was also participating in the DARPA SUB-T challenge with the CoSTAR
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experience planning & developing projects preferred. Extensive knowledge and expertise in molecular biology, biochemistry, cancer biology, and animal models (mice) preferred. Requires successful completion
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Corporation (GRDC)’s initiative, multiple PhD Scholarship positions have been made available on developing new data analytics methods and modelling for grain research innovation and ensuring enduring