614 embedded-system-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Monash University
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to reproducible research, critical analysis, and publication Experience with deep learning, audio analysis, or affective computing is advantageous but not mandatory.
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responsible for end-to-end program planning, coordination and delivery, ensuring training is implemented to agreed quality standards and contractual requirements. Acting as a key liaison between the MIRRA
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accurately and on time. The position also oversees resources, stakeholder engagement and team coordination, while providing strategic advice and strengthening program systems and processes. We are seeking
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University as our next Media and Communications Adviser and play a leading role in shaping and amplifying the University’s global narrative. This is a unique opportunity to position one of the world’s leading
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people who discover them The Opportunity The Faculty of Engineering, Department of Mechanical and Aerospace Engineering, is seeking an exceptional Research Fellow – Acoustic Sensing to join an innovative
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University is committed to increasing university participation of students from communities under-represented in higher education. As part of this mission, we are seeking an experienced and dynamic Senior
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Monash Indigenous Research Award Monash Indigenous Research Awards (MIRA) is part of a support package provided by Monash University to support Indigenous Australian students undertaking Research
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contributions, lived experience and expertise. That’s why we champion an inclusive and respectful workplace culture where everyone is supported to succeed. Learn more about Monash . Be part of our story. Work
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The Opportunity The Monash Research Office is seeking an experienced research administration specialist to join our team as a Senior Awards Officer in the post-awards service area. We are looking for a strategic
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The primary objective of this project is to enhance Large Language Models (LLMs) by incorporating software knowledge documentation. Our approach involves utilizing existing LLMs and refining them