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Field
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including machine learning. This research will support the path to net zero flights and there will be opportunities to become involved in practical aspects of fuel system design and testing during their PhD
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profile (rate and localization). This work focuses on machine learning assisted PSPR optimization of recently developed lean Mg-0.1Ca alloy produced by PBF-LB. After identification of the most relevant
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frequent cloud contamination. This scale mismatch prevents a coherent representation of radiative–thermal processes at the urban scale. This PhD will develop physics-informed deep learning models for data
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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease
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photonics or electrical engineering or applied physics or similar. Experience with machine learning, digital signal processing and experimental work is a plus. Further obligations: The doctoral candidate (DC
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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international experience during their PhD. We offer a PhD student position to explore and develop Machine Learning models evolving over space and time and to make use of these models to understand
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based models, including the deployment of machine learning algorithms. The project aims to have a tangible impact on the way urban waters are monitored, and the findings of your project will be
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qualifications You have graduated at Master’s level in computer science, computer engineering, human-computer Interaction, media technology, visual learning and communication, or closely related fields
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management, and machine learning approaches for process monitoring and control For this function, our Brussels Humanities, Sciences & Engineering Campus (Elsene) will serve as your home base.