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to make viable trading decisions under high price volatility. This PhD position focuses on designing, developing, and evaluating self-learning energy trading algorithms that are able to cope with
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work hands-on with clinical data and build robust deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image
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In this role, you will help develop and implement cutting-edge AI solutions for real-time, image-guided medical applications, with a focus on advanced robotics. You will work directly with clinical
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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ICs. Development cycle including circuit design, simulation, modeling, layout, verification and measurements Design high power-efficiency RF power amplifiers Develop scripts and algorithms for analog IC
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for the physical sciences. We have a strong profile in computational statistics, simulation and learning algorithms, and scientific software development. As a closely collaborating, international team
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components: - operational modal analysis to extract the modes of the probed medium, - algorithmic and experimental developments on the MSE method - and algorithmic and experimental developments on the MFP
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of this role is to support and contribute to an industry innovation research project. The Research Engineer will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep
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data on homeowner retrofit needs and preferences. Undertaking research trials to test and refine the AI algorithms used in our platform. Meaningful assistance in research and policy development with a
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assist the project leader in the research project - “Enabling bulk data transfers for Low-Power Wide-Area Networks”. He/She will be required to: (a) develop innovative solutions to improve