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Candidates should hold a previous degree (Bachelor’s and/or Master’s) in Computer Science, Data Science, Robotics, Mechatronics, or Software Engineering, with demonstrated knowledge in machine
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. This is a very broad topic that allows the student to choose a scope (e.g. the nature of the technology, participant demographics, visualisation) of interest to them.
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their potential at Monash University. The scholarship program amplifies diversity in STEM through empowering scholarship recipients to achieve academic success. Total scholarship value $6000 Number offered 10 See
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Join our inclusive collaborative community driving advances in mental health science Be surrounded by extraordinary ideas - and the people who discover them The Opportunity The Turner Institute for Brain
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outputs of a team dedicated to translating discovery into meaningful impact for people living with Friedreich Ataxia. We are seeking someone with a PhD in computer engineering, biomedical engineering
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biology models Testing and evaluating new therapeutics in support of a drug discovery program Delivering high-quality, timely data to the MTDD–Servier joint project team Supporting project milestone
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science to improve health outcomes for women and their families in public healthcare settings. Your PhD research will form part of a wider research program focused on implementing lifestyle improvement
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About the Opportunity The Faculty of Engineering, within the Department of Materials Science and Engineering, is seeking a standout Research Fellow to join our team. In this role, you will play a key part
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The world is dynamic, in constant flux. However, machine learning typically learns static models from historical data. As the world changes, these models decline in performance, sometimes catastrophically so. This PhD will develop technologies for addressing this serious problem, building upon...
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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning