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capital markets, financial modelling and valuations Exceptional management and leadership experience Highly developed numerical, conceptual and analytical skills, and technical expertise to resolve complex
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DNA or RNA motif discovery is a popular biological method to identify over-represented DNA or RNA sequences in next generation sequencing experiments. These motifs represent the binding site
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Dowe, 1999a) ensures that - at least in principle, given enough search time - MML can infer any underlying computable model in a data-set. A consequence of this is that we can (e.g.) put latent factor
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research in digital sociology, ethnographic methods, and human-AI relationships. Your responsibilities will include: Conduct research, analysis, and publication targeting diverse audiences, including
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anomalies in evolving graphs. In this research proposal, our aim is to explore the parallels of deep learning and anomaly detection in dynamic graphs. In particular we are interested to redesign deep neural
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in diverse, real-world environments. Both classical machine learning methods and deep learning techniques can be employed to tackle this task. This project aims to achieve several objectives: 1
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Remuneration: The successful applicant will receive a tax-free stipend, at the current value of $35,013 per annum 2024 full-time rate, as per the Monash Research Training Program (RTP) Stipend . Monash
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Project Description Recent advances in mixed reality (MR) technology, which seamlessly blend the physical environment with computer-generated content around the user, have reduced the barriers
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tissues or reveal micro- or nano-structural features, like the small air sacs in lungs. To overcome these limitations, alternative X-ray imaging methods have been developed: X-ray phase-contrast and dark
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computer skills with experience using Microsoft Word, Excel and PowerPoint; and specific experience in working with a range of data management/analytical software. Prior experience, knowledge and expertise