299 phd-in-architecture-and-built-environment-"Prof" Fellowship positions in Australia
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to work alongside the brightest minds, who have joined us from all over the world, and within an environment where interdisciplinary collaborations are encouraged. At the core of our teaching remains our
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PRIMARY DETAIL • Salary package: $106,089 (Level A.6 - PhD) to $113,697 p.a., plus 17% employer’s superannuation and annual leave loading. • Appointment Type: Full-time, fixed Term for two years
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the environment through innovation in designing, constructing, and maintaining the natural and built environment. To be successful you will need: A track record of research, relative to the career opportunity in
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features in alloys, to aid future alloy design. The UNSW team, lead by A/Prof Patrick Burr, will provide the modelling contributions, and the team at USyd, led by Prof Julie Carney and Dr Ranming Liu, is in
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community, you will have the opportunity to work alongside the brightest minds, who have joined us from all over the world, and within an environment where interdisciplinary collaborations are encouraged
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economy ecosystem” The sub-projects are ‘Building public trust in upcycling’ and ‘Designing upcycling business ecosystems’. You will conduct independent and collaborative research with Prof Jo Lindsay and
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technology to support all patients with heart failure. Our program is co-directed by Prof Shaun Gregory (Queensland University of Technology) and Prof David Kaye (Monash University). About us QUT is a major
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understand that our reputation has been built on the dedication and expertise of our staff and we offer a dynamic and diverse working environment with opportunities to grow and develop careers. We believe
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, implementing, and optimising advanced neural network architectures tailored to biological sequence and network data, with a particular focus on RNA-mediated gene interactions. Applying interpretable machine
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Australian National University | Canberra, Australian Capital Territory | Australia | 42 minutes ago
, approximate inference, deep learning, or Bayesian optimisation are encouraged to apply. Interpretable Machine Learning for Natural Language – Led by Prof Lexing Xie, this stream applies machine learning