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Lecturer / Senior Lecturer in Ecology or Evolutionary Biology Job No.: 679705 Location: Clayton campus Employment Type: Full-time Duration: Continuing appointment Remuneration: $123,138 - $146,228
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This PhD project focuses on the design and evaluation of hybrid quantum–classical algorithms for large-scale data analytics and optimisation problems. The research will investigate how quantum
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guarantees of FL. In this project, we aim at an ambitious goal - designing secure and privacy-enhancing algorithms and framework for FL and applying our designs into real-world applications. To achieve
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package should be prioritised are surprisingly difficult computational tasks. State-of-the-art high-performance algorithms are used to calculate routes for the vehicles in order to minimise costs and
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This research aims to design a sustainable framework for optimizing distributed computing systems to enhance performance while minimizing energy consumption. Existing scheduling algorithms often
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validate wearable data algorithms, software tools and pipelines supporting large-scale, multi-cohort research. It will contribute to the ProPASS federated data analysis platform, including testing
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estimation, confidence calibration, model routing, token selection, early exiting, adaptive visual processing, and efficient use of multiple foundation models. The work will combine algorithm development with
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human-centred embodied AI New methods for modelling human behaviour and interaction dynamics Multimodal temporal and predictive learning algorithms Experimental evaluation against strong contemporary
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Optimisation methods, such as mixed integer linear programming, have been very successful at decision-making for more than 50 years. Optimisation algorithms support basically every industry behind
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scheduling, resource allocation, and workload placement algorithms for distributed AI training; Improve the communication efficiency, scalability, and reliability of decentralised training frameworks; Evaluate