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Field
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-Based Modelling ✔ Data Science & Advanced Analytics with real-world SCADA data ✔ Renewable Energy Systems & Wind Turbine Engineering ✔ Industry Engagement & Research Commercialization ✔ Scientific Writing
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Requirements We will only consider candidates with a good Bachelor’s degree (ideally, a first, minimum 2:1) and/or a master’s degree in computer science, data analytics, health economics, or computational
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chemistry) laboratory; statistical skills for proper data treatment ; experience with programming (R or Python) for data handling and visualisation; strong analytical skills; excellent communication skills
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imaging. Strong analytical skills and experience with medical imaging technologies and data analysis. The ability to work collaboratively in multidisciplinary teams and communicate effectively across
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application, polygraph data capture and analysis has received limited systematic research and does not yet incorporate modern sensors, computing and analytical techniques. Project: This project aims to explore
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they would design the project (for more information, see the ‘Application procedure’ below). Application Procedure Informal enquiries are welcome and can be made directly to Dr Christiane Ahlborn, Assistant
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to impart both scientific and methodological knowledge and offers the opportunity to regularly present doctoral projects in internal events and benefit from scientific exchange. You can find information
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to impart both scientific and methodological knowledge and offers the opportunity to regularly present doctoral projects in internal events and benefit from scientific exchange. You can find information
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in the Danish energy sector. As a PhD candidate, you will join the Energy Markets and Analytics (EMA) Section within the Division for Power and Energy Systems (PES). The EMA Section is renowned for its
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Knowledge of electronics, programmable logic, and processing systems is an advantage Strong analytical and programming skills are required (Python, VHDL, Matlab, and C/C++). Prior proven experience in data