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Field
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% teaching component.) The Role The Macquarie University Lighthouse Fellowship (MQLF) supports researchers within three years of the award of their PhD who have an outstanding track record (relative
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, yielding negligible performance gains or even inducing catastrophic forgetting. To bridge the gap between theoretical AL and real-world deployment, this PhD project will develop resilient active learning
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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community and thrive in this role, you will likely have the following skills and experience: Qualifications PhD in Computer Science, Engineering, or related discipline Strong publication record in top-tier AI
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education across programmes at all levels: Bachelor, Graduate Diploma in Psychology, Honours, Master of Clinical Psychology, Master of Science in Coaching Psychology and Higher Degree Research tracks (PhD
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education across programmes at all levels: Bachelor, Graduate Diploma in Psychology, Honours, Master of Clinical Psychology, Master of Science in Coaching Psychology and Higher Degree Research tracks (PhD
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inference. PhD Program The project is based in the Centre for Health Economics, a large and active economics research group within the Monash Business School in Melbourne, Australia. As a candidate in the CHE
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integrity. As such, we see the importance of recruiting talent aligned to these values and are looking for a Postdoctoral Research Associate who has: a PhD in mathematics, applied mathematics, computational
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disciplines: Data Science, Artificial Intelligence and Cloud Systems and Security. Our PhD students and faculty conduct world leading research in many areas of Computing, including in: Artificial Intelligence
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these decisions? Required knowledge This project is open to candidates from diverse academic backgrounds, including computer science, data science, learning sciences, or educational technology. While prior