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ideas. To develop algorithms, machine learning models, Python modules, demonstrators and training pipelines for publication and translation into commercial products that can be widely and reliably adopted
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frameworks (MOFs), and related materials using hybrid classical-quantum algorithms. A key component of the role involves using first-principles methods that capture strong electronic correlations, such as DFT
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., NumPy, OpenCV, scikit-learn). Experience implementing and evaluating state-of-the-art tracking algorithms such as DeepSORT, ByteTrack, and Transformer-based approaches. Proven ability to design and run
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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning algorithm for photovoltaic applications and utilising them for the investigation
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-based hail climatology for France, Switzerland and Northern Italy, including hail swaths per event across more than a decade. Design, develop and train geostationary satellite-based hail algorithms using
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and innovation catalyst, in this exciting project, you will develop novel algorithms to monitor and analyse workers' movements, detect harmful movement patterns, and implement simple intervention
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: Research: Development and validation of predictive maintenance algorithms for solar farms. Interface with industry partners for knowledge sharing and feedback. Play a key role in reporting to the funding
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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experience in one or more of the following areas: machine learning, reinforcement learning, algorithmic trading, or data-driven modelling. Excellent communication skills: Solid written and verbal communication
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postdoctoral researcher with: A PhD (or near completion) in Computer Science, Computational Biology, Mathematics, Bioinformatics, or a related discipline. Proven expertise in machine learning and algorithm