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inverters to enhance grid flexibility, reliability and stability. • Apply machine learning and AI tools for the battery system health estimation and maintenance prediction and integrate analytics
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writing and presentations at conferences and workshops. Qualifications: PhD in Biomedical Engineering, Medical Robotics, Computer Science or a closely related field Strong background in medical image
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of strains) to in-field testing of up to 800 strains. The scale and standardized approach will create a unique foundation for advanced data analysis, including AI, machine learning, and statistical modelling
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
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the department We seek a candidate with the following qualifications : Required: PhD in wildlife ecology, conservation biology, or a related field Experience with technology-assisted wildlife monitoring (e.g
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hold a PhD in oceanography, marine ecology, computer sciences, data sciences or similar. We expect that you have: Expert knowledge on network modelling, especially aimed at ecological applications Strong
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
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within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer
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following thematic areas: • AREA 1: Machine learning and AI-driven methods for design, simulation, and optimisation in architectural and construction engineering. • AREA 2: Robotic and additive
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internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering