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Field
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systems Strong skills in data-driven analysis and modelling, simulation, control, and validation Familiar with modeling of PtX and storage technologies, model predictive control, machine learning
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project activities and writing reports Your Profile MSc or PhD in a relevant field Experience with Machine Learning modeling Coding experience, preferably Python Basic knowledge of databases tools and
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analysed by bespoke machine-learning driven algorithms, combined with physical models, to de-noise images, identify features and correlate properties, giving critical insights into power loss pathways
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emerging spectrum (FR3, MMW, sub-THz/THz) Extreme massive MIMO communications Analog, digital and hybrid beamforming architectures Reconfigurable intelligent surfaces Machine learning for wireless
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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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for candidates to have the following skills and experience: Essential criteria PhD qualified in mathematical, physical or computational sciences Experience in using machine learning methods to analyse datasets
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experience with machine learning frameworks such as PyTorch or scikit-learn. You are a team player and a team leader: you can mentor PhD students and Bachelor students effectively. You share your knowledge
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the outcomes of SCC surgery. Job Responsibilities: As a PhD candidate, you'll focus on: Develop cutting-edge AI models: Train state-of-the-art deep learning models to segment SCC and healthy tissues using both
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comes with no teaching obligations BASIC RESPONSIBILITIES AND OBLIGATIONS Conducting research related to the scientific project titled “Calculus of variations in machine learning problems”, in particular