210 algorithm-development-"Multiple" "NTNU Norwegian University of Science and Technology" PhD positions in Australia
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for Robotic and Autonomous Perception This project aims to make robotic perception systems safer and more reliable by developing new techniques that continuously monitor the performance of their machine
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the academic staff at SIT. We are looking for PhD students to work on projects on stochastic optimisation algorithms for hyper-parameter tuning in Machine learning. The successful candidate will explore
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examine emotional processing during Emotion Focused Therapy (Watson & Sharbanee, 2022; Greenberg & Johnson, 1988). The specific focus of the project will be open to being developed in collaboration with
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This project aims to develop outcome measures for early detection of neurodegenerative disorders using artificial intelligence (AI) and various sensing modalities, offering personalised support to
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) implement the COMPAS survey across two waves at St John Ambulance, (c) develop a predictive algorithm that can predict suicidal intentions and behaviours 12 months later, (c) use the algorithm to stratify
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-tracking, pupillometry), cognitive modelling, and regulatory analysis to assess how algorithmic explanations shape human judgement and how existing legal and ethical frameworks align with the evolution
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, in partnership with Bower Place, will examine the enablers of meaningful collaboration from the perspective of multiple stakeholders that are required to achieve positive mental health and wellbeing
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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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future autonomous instrument control and self-directed experimentation will be developed, recognizing the challenge presented by the integration of multiple complex systems. Coding and user interface
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models