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Mixed-Integer Programming (MIP) solvers are very powerful tools to solve combinatorial problems that arise in many industries. Modern MIP solvers usually run a sequence of algorithms to solve
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broad range of topics: from model-predictive building control and community battery integration to wind farm optimisation and multi-decade investment planning, we support clever algorithms and data
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quantum engineering, quantum computing, and/or quantum information sciences A deep understanding of quantum algorithms and/or experience with quantum hardware will also be beneficial. A passion for
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Research Training Program (RTP) Stipend www.monash.edu/study/fees-scholarships/scholarships/find-a-scholarship/research-training-program-scholarship#scholarship-details This opportunity invites applications
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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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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Industry Innovation Program Scholarship The Embedded Co-Op Scholarship funded by an Industry Partner via the corresponding Faculty be introduced to allow industry and students to directly interact
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This project aims to develop robust algorithms capable of identifying and analyzing fingertips extracted from both static images and video footage. Machine learning techniques, particularly computer
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the headspace website. Possible approaches to addressing this challenge might include: Developing algorithms to identify patterns and preferences based on service users’ previous content engagement
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experiments for months before the value of output y is measured for some given input x. This creates an exciting challenge for AI researchers to develop smart algorithms that can find the optimal value of input