310 computer-programmer-"https:"-"IDAEA-CSIC" "https:" "https:" "https:" "https:" "U.S" uni jobs at Monash University
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program (or ability to foster research collaboration) is considered along with records of achievement. Those with joint Monash-Museums Victoria supervision will be highly regarded. To retain
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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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. Required knowledge Strong background in machine/deep learning, computer vision, or applied statistics. Solid programming skills in Python and experience with deep learning frameworks (e.g., PyTorch
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• enhancing nutrition and protecting the microbiome • improving symptom control and psychological wellbeing As the Project Manager, you will support the end to end delivery of this ambitious research program
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collaborative team at Monash Rural Health Rural Health Placements Officer role supporting student placements across the MD program The Opportunity The Rural Health Placements Officer offers an exciting
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immersive and educative programs and initiatives within Parbinata, ensuring culturally grounded program design, sustainability and long-term impact. Oversee the end-to-end design, and delivery of significant
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healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series
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computational techniques can be combined with classical systems to improve performance, scalability, and solution quality for tasks such as: Similarity search and nearest-neighbour queries Graph and routing
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supporting our mission. Headquartered at Monash University, the Centre is a transdisciplinary, multi-stakeholder program aiming to mobilise survivor-centric and Indigenous approaches, interdisciplinary