232 design "https:" "https:" "https:" "UNIVERSITY OF THE SUNSHINE COAST UNISC" PhD positions in Netherlands
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, biomaterials design, bottom-up tissue engineering, and musculoskeletal tissue regeneration, ensuring a highly supportive and intellectually stimulating environment for PhD training and scientific development
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? No Offer Description Do you have a passion for nanomaterials, colloidal self-assembly, and sustainable functional materials? Are you excited about designing new photonic materials from renewable
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into answering counterfactual questions. Using remote sensing multimodal time-series data and Earth foundation model embeddings, you will design and develop causal machine learning models tailored for dynamic
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collaborate intensely with a unique consortium, including all actors in the consumer electronics value chain: Original Equipment Manufacturers (OEMs), (design) consultancies, retail, repair organisations
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with support by the other team members Dr. Laura Righetti and Prof. Han Zuilhof. The PhD project is a collaboration with Wageningen Food Safety Research . Where to apply Website https
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telecommunications networks and urban infrastructures Change: Developing data analysis and modelling methods to understand the interdependency Impact: Better design to enhance telecom and urban performance Job
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)national scientific meetings Writing scientific papers Work in an interdisciplinary team with other students and scientists to discuss, plan and perform research Finish the project with a scientific
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proclaimed sustainability goals. Specifically, the PhD position at Utrecht University focuses on how environmental and design requirements, such as sustainability standards, material choices, and architectural
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environmental and design requirements, such as sustainability standards, material choices, and architectural competitions, are negotiated within densification projects. By revealing how discourses around land
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to certify performance and quantify uncertainty, Attack-tolerant and resilient control design, explicitly accounting for disturbances, adversarial inputs, and model mismatch, Hybrid physics–ML monitoring