379 distributed-computing-"U"-"Washington-University-in-St" positions at Monash University
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largely opaque to computational analysis, into a transparent phenomenon from which selected features can be captured and rendered visible for the purposes of professional development for teachers. Depending
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. Amandeep Kaur, you will contribute to a vibrant research program centered on the design and development of novel fluorescent probes for super-resolution imaging—a powerful technique revolutionizing how we
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Training Program (RTP) Stipend www.monash.edu/study/fees-scholarships/scholarships/find-a-scholarship/research-training-program-scholarship#scholarship-details Be inspired, every day Drive your own learning
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schools as part of the Access Monash Mentoring Program, giving you the opportunity to develop your leadership, public speaking and teamwork skills. The Gandel family have a close connection with Monash
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meetings and related processes, and undertake initial and ongoing training and education Demonstrate effective communication and interpersonal skills, as well as computer skills Be able to read, interpret
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-supervised contrastive learning to encode the EEG data. This way we can better model the distribution of our EEG data before classifying it to different mental status. See a recent work here [1]. The data will
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Lecturer - Learning Design and Technology Job No.: 680408 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: $118,974 – $141,283 pa Level B (plus 17% employer superannuation) Amplify your impact at a world top 50 University Join our inclusive,...
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analysis. Additionally, experience in healthcare informatics, user experience research, and a commitment to improving mental health services are highly desirable. Project funding Project based scholarship
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comprising one or more of: a bachelor’s honours degree in a relevant discipline (computer science, software engineering), which includes a research thesis or project, with an overall average grade equivalent
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., Pan, S., Aggarwal, C., & Salehi, M. (2022). Deep learning for time series anomaly detection: A survey. ACM Computing Surveys.