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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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geospatial data on fisheries activities near OWFs to evaluate the distributional impacts of OWFs across different fleet segments; conducting fieldwork and surveys in collaboration with ‘NO REGRETS’ partners
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meals for field teams, and renting and driving vehicles. Collecting and processing data, and ensuring regular backups to RUG and KNIR storage systems. Short stays at the KNIR Institute in Rome may be
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PhD position on Closed-loop testing for faster and better EM evaluation of complex high-tech systems
with antennas, evaluate different algorithms for EM field strength data, investigating the minimal needed sensors (up to nine), and controlling the equipment using in-line measured data (closed-loop
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PhD candidate in Design and synthesis of pyrophosphate mimetics to study enzymes involved in natural
break. For more information, see our website. Faculty The Faculty of Science at Leiden University is a world-class faculty where staff and students work together in a dynamic international environment. It
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spectroscopy data and AI, to automatically identify textile fabrics with high accuracy in real-world sorting conditions by (1) defining optimal spectral bands, spatial resolution, and acquisition speed; (2
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to collectively explore urban futures? This position is hosted by the Faculty of Geo-Information Sciences and Earth Observation (ITC) within the Sector Plan Beta II program’s focus area on Spatial Dynamics, which
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the project, primary data collection will need to be done in Southern Kenya (Narok and Kajiado counties). Maasai Mara University in Narok county will provide on-the-ground assistance. The PhD position is
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, innovation Researcher Technical and laboratory You are signed up for the Job alert Previous Next Sign up Cookies We use cookies and similar technologies to process your personal data (e.g. IP address
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testing, to achieve sub-second cycle times for robotic systems. 3. Demonstrated analytical problem-solving through experimental design, critical quantitative and qualitative data analysis, and validation