14 big-data-and-machine-learning-phd PhD positions at Swinburne University of Technology
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Opportunity to help develop a novel nanophotonic therapy targeting drug-resistant hypertension Full-time, fixed term (3.5 years) position at our Hawthorn campus Annual stipend $40,000 About the Role We are seeking a talented and motivated graduate in biomedical engineering, mechanical...
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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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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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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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Applied mathematics, fluid mechanics, high-performance computer simulations. Two full time, fixed term positions (3 years) at Hawthorn campus $34,700 per annum (2025 rate) About the Scholarship
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Centre for Forensic Behavioural Science Full time, fixed term position at Alphington, Victoria Stipend of $40,000 p.a. for 3 years About the Scholarship We have an exciting PhD scholarship
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groups, and an awareness of security and ethical issues associated with working with health and sensitive data. A full list of selection criteria is available within the position description. About
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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