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to: Design computer vision algorithms to detect and classify key clinical activities during birth and neonatal resuscitation, including object detection and tracking Develop temporal analysis techniques
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to submit a professional development plan no later than one month after commencement of the postdoctoral period. It is expected that the successful candidate will be able to complete the project in
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous
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technology management, or smart grids. Experience in development of mathematical meta-models, control strategies, optimization methods and algorithms, data analysis and machine learning techniques, techno
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learning arenas. Symbiosis aims to reinforce the foundations for responsible, trustworthy, and sustainable use of AI in our educational institutions by developing ethical and sustainable principles to guide
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innovative material solutions. This project tackles this challenge head-on. The project vision is to develop a pioneering AI-driven methodology for designing Functionally Graded Materials (FGMs) specifically
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/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines for plausible narratives of regional climate change, novel algorithms for rare
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collaborative skills. Applicants must be proficient in both written and oral English. Experience from one or several of the following areas is an advantage: Developing algorithms for CFD solvers (e.g. OpenFOAM