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cellular reprogramming strategies and identify new omics-based biomarkers. We work closely with clinical partners and we focus on deep understanding of molecular mechanisms of disease development. PhD
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progression modelling, exploiting advances in deep feature learning and uncertainty quantification to support the Bayesian framework, as well as implementation of computational models of neurodegeneration
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graduate and professional learning programs Experience with online education and online education programs Deep knowledge of graduate and continuing education learning and best practices Outstanding
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School, College and University committees, and may also lead a discipline area or strategic learning and teaching portfolio. To be successful in this position, you'll have: PhD or demonstrated equivalence
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development of deep learning tools applied to different types of genomic data, proteomics, expression data, immune profiling, metabolomics data, or general health data. You will have a proven track record of
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the world’s largest supercomputers (Polaris, Aurora) and some of the most advanced characterization tools in the world at Argonne and Sandia National Labs. Candidates with a background in deep learning
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of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. Fuelled by curiosity and a deep
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Apply now Job no:502129 Work type:Full time Location:Launceston Categories:Balanced Research/Teaching Coordinate, develop and teach high-quality coursework Effectively contribute to research
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of published research relevant to clinical medicine, especially those using quantitative research methodologies. Experience and demonstrated achievement in University-level teaching and learning. A record of
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of receiving their PhD. In particular for this position, the following is required: PhD in data science, AI, computer science, machine learning, Earth system science, climate etc., with a thesis subject relevant