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, commensurate with experience: Level A: $87,266 - $118,416 p.a. plus 17% superLevel B: $124,656 - $148,023 p.a. plus 17% super Lead development of ecosystem level biodiversity metrics and collaborate with
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on application logic rather than underlying algorithm development), and the capability of independently driving the full "Data-AI-Deployment" process; (c) the ability to evaluate the fit between AI applications
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London School of Hygiene & Tropical Medicine; | London, England | United Kingdom | about 15 hours ago
data science approaches to generate evidence and tools for safer, more targeted infection management in hospitalised newborns. The post-holder will lead the design, development, deployment and evaluation
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hybrid work agreement. The salary range for this position is $65,000 - $75,000 per annum, plus benefits. The primary goal of the CMMI is to develop an integrated multi ‘omics platform to investigate
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You will have the opportunity to join a research group which has a long history of developing novel polymers for drug delivery and medical device applications within the new School of Applied
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to support you in the development of your independent research and innovation career. As part of the programme we will offer you flexible support, including induction, mentoring and career development planning
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on the assurance, risk assessment, and insurability of AI systems, supporting the development of trustworthy, reliable, and accountable AI technologies across critical sectors such as finance, insurance, and digital
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opportunities for professional development across this two-year project. We are seeking a Postdoctoral Research Associate to support the University of Birmingham’s contribution to the Horizon Europe funded
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This PhD scholarship is funded by an Australian Research Council Industry Fellowship grant. It is a 3.5-year research training program. The ARC Industry Fellowship program aims to develop a strong
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fields. You will be an integral member of an inter-disciplinary Science of Learning research team in developing brain-based machine-learning predictive models for early identification of mathematical