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Job Description Job Alerts Link Apply now Job Title: Research Associate/Fellow/Senior RF - Net Zero Healthcare Delivery Posting Start Date: 15/07/2025 Job Description: Job Description Climate change
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needs. In addition, your role might involve supporting existing initiatives, organising workshops, proposal development and writing. PhD in Engineering, Physics, Mathematics, Computer Science or a closely
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collaboration with laboratories computational biologists. Lead independent research projects and contribute to collaborative efforts within the lab. Present findings in manuscripts, conferences, and funding
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:32984792 [Lancet Digital Health 2020]), and network medicine (PMIDs:35572351 [Nature Aging 2021], 31375661, 30002366 and 30867426 [Nature Communications 2018, 2019a and 2019b]) to address challenging
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: Autonomous Systems and robotic platforms Agricultural Robotics and precision agriculture technologies Artificial Intelligence and Machine Learning applications Computer Vision and sensor integration
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and co-editing research vision and policy documents The post holder will engage in developing and leading research projects contributing to IDAI's interlocking thematic networks, supporting a
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 6 days ago
on developing new methods, whether experimental, analytical, or computational. The Experimental Petrology group at RSES is a world-leader in high pressure experimental petrology and the application of cutting
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project - “Advanced integrated co-packaged optics transceiver module for high-speed network applications”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must
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About us This vacancy sits within the Institute for Sustainable Resources (ISR) within the Bartlett School of Environment, Energy, and Resources in the newly created Centre for Net Zero Market
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where