45 engineering-"https:"-"https:"-"https:"-"https:"-"https:"-"INSA-RENNES" PhD positions at Monash University in Australia
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scholarships funded by a national elite HDR training program: Data61 Next Generation Graduate (https://www.csiro.au/en/work-with-us/funding-programs/programs/next-generation-graduates-programs), and
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computational project. To do this project you would need to apply for a Monash Scholarship. Required knowledge A decent theoretical background in physics, computer science, mathematics, engineering
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computational project. To do this project you would need to apply for a Monash Scholarship. Required knowledge A decent theoretical background in physics, computer science, mathematics, engineering
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[Malaysia Campus- VPSSP] An AI-Informed Planetary Health Framework for Equitable AMR Risk Mitigation
ensure local relevance. Required knowledge Artificial Intelligence; Machine Learning; Computer Vision; Bioinformatics; Biomedical Engineering; Neuroscience; Genomics; Medical Physics; Data Science
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knowledge A decent theoretical background in philosophy, physics, computer science, mathematics, engineering, neuroscience or psychology. Project funding Other Funding reference https
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knowledge A decent theoretical background in philosophy, physics, computer science, mathematics, engineering, neuroscience or psychology. Project funding Other Funding reference https
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publications, citations or scientific careers to test the implications of a model. References Fortunato, S. et al. “Science of science.” Science 359 (2018): eaao0185. https://doi.org/10.1126
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specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating
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specialised as-needed via fine-tuning or prompt engineering. In this project we will explore all aspects of this process with a focus on increasing trust in the model outputs by reducing or eliminating
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from an unbiased strategy space.” Journal of the Royal Society Interface 16 (2019): 20190127. https://doi.org/10.1098/rsif.2019.0127 Perera, I., de Nijs, F. and García, J. “Learning to cooperate against