13 phd-studenship-in-computer-vision-and-machine-learning PhD positions at Swinburne University of Technology
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nanoparticles in drug delivery or diagnostics, familiarity with cellular targeting, and use of computational tools to model nanoparticle behaviour. Experience in a regulated research environment is also
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chemoreceptor activity. Your work will bridge both computational and experimental research, including validation using in vivo models, optimising light delivery systems, and aligning in vitro and in vivo findings
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) and computer simulation (FEA) Experience in material characterisation and experimental testings Knowledge in impact dynamics Passionate and have interest in pursuing PhD degree. Experience in research
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. • Proficient computer skills, including competence in the use of MS Office and other software packages, especially word processing, database and spreadsheet skills. About Swinburne University of Technology
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Centre for Quantum Technology Theory (CQTT) Full-time, fixed term (3 year) position at our Hawthorn campus Annual stipend $34,700 About the Role We are seeking a highly motivated and talented PhD
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are seeking a highly motivated and enthusiastic candidate with a strong interest in computer vision, AI, and robotics. The ideal candidate will have solid programming skills, particularly in Python, and be well
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to peer-reviewed academic publications Qualifications Completed undergraduate degree in physics, computer science, machine learning, computational modelling, or similar. About Swinburne University
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health services. The PhD scholarship is available for a highly motivated and competitive candidate enrolled in a PhD program to undertake research that supports the research agenda of the project
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Local students applying for a place in a higher degrees by research (HDR) program will automatically be considered for an Australian Government Research Training Program (RTP) Fees-Offset (Domestic
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and Prof Kath Hulse (Swinburne). This PhD project will analyse the role and mechanisms of social communication, learning and social networks in fostering sustainable and energy efficient household