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mathematical and physics-based models of complex thermal systems. You will develop reduced order representations enabling real-time implementation suited for online optimization of operating conditions and
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optimization of machine learning methods. Candidate’s profile An ideal candidate would typically have: a strong degree or higher qualification in a relevant field (e.g. computer science, mathematics
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external phenomena on optical signal, and network-wide optimization of transmission and sensing parameters, as well as resource assignment, lie at the core of this project. Who we are looking for The
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successful candidates will develop fundamental theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization
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Python, and familiarity with relevant machine learning frameworks. A solid understanding of mathematical modeling, optimization, or numerical methods. Experience in one or more of the following areas is
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engineers learn together. You’ll build a shared foundation in quantitative trading, programming, data analysis, mathematical modeling, and modern machine learning and AI tools before applying those
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science, mathematical engineering, robotics, or a closely related field. The candidate should have solid mathematical and analytical skills and a strong interest in the interplay between wireless systems
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study programs. The department has around 200 employees, approximately 50% coming from abroad. In the Management Science Division, the research ranges from mathematical-based optimization models to
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theory and practical algorithms that move reinforcement learning closer to real-world systems. Position 1 will focus on trajectory-centric optimization, Position 2 on adaptive policies, and both will
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Australian National University | Canberra, Australian Capital Territory | Australia | about 21 hours ago
, even when they might appear conflicting. This project aims to forge an advanced framework uniting data science, machine learning, simulation, multi-objective optimization, and uncertainty-based