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. The PhD student is expected to develop and apply methods for causal inference will be part of the SMARTbiomed Pioneer centre https://SMARTbiomed.dk/about-SMARTbiomed. Project Description The PhD project
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circuit models and algorithms for estimating the charge, health, and power based on direct methods (e.g. open circuit voltage), model-based methods (e.g. Kalman filtering), data driven methods (e.g. machine
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be to develop wireless sensing and communication methods that are designed together with AI-based inference, rather than treating connectivity as a separate layer. Particular attention will be given
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the position. Your work tasks You will develop and validate digital-twin and optimization methods for electrolysis systems, working both independently and collaboratively with the group and with academic and
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interventions and commercial marketing measures, consumer-oriented innovations, and social dynamics can be combined to support sustainable change. Please make sure to read more about the project here https
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models of steel structures with emphasis on fatigue hot-spot modelling, building on existing in-house methods Load and stress estimation using virtual sensing techniques (e.g., Kalman Filters) combined
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, especially but not exclusively related to AI, https://digitalcurriculum.au.dk in collaboration with colleagues from CED and NAT Join as a co-teacher in a few of the departments’ workshops and teaching
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experimental methods in thermal and fluid mechanics, including data analysis, uncertainty estimation, and model validation, is desirable. Familiarity with optical measurement techniques in fluid mechanics and
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Biogenic volatile organic compounds (VOCs) have a profound impact on local and regional climates. Yet, there is high uncertainty of this impact due to the lack of reliable estimates for natural
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sensing technologies (camera, Electromagnetic tracking system, FBG sensing) with continuum robotic systems and development of associated sensing and estimation algorithms 3D environment reconstruction based