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, you will be an active member of the SDL “Fluids & Solids Engineering” and will collaborate strongly with the SDL “Applied Machine Learning”. You will have the following tasks: You will work together
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evidence-based practices to effectively engage students from a range of backgrounds and experiences. Preferred Education and Experience: A PhD or equivalent in engineering or computer science or a related
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for highly motivated candidates currently enrolled in a Master’s degree or engineering program in applied mathematics or computer science. Candidates should have a solid background in machine learning and be
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teaching staff are world leading and world building as they advance knowledge and learning. For more information on our school go to the following link - https://www.unsw.edu.au/engineering/our-schools
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enrolled in an official Telecom/Electrical Engineering/Computer Science/Geomatics Master program that provides subsequent access to a PhD program according to the Spanish legal regulations. The last official
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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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(see http://orcid.org/ ) Teaching portfolio including documentation of teaching experience Academic Diplomas (MSc/PhD) You can learn more about the recruitment process here . Applications received after
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together to develop solutions for the global challenges of today and tomorrow. Where to apply Website https://academicpositions.com/ad/eth-zurich/2026/phd-position-in-mechanochemica… Requirements Research
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Intelligent active gate driver for net zero energy systems (S3.5-ELE-Foster) School of Electrical and Electronic Engineering PhD Research Project Competition Funded Students Worldwide Prof Martin
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following themes: Traffic modelling and multimodal transport simulation Travel behaviour and choice modelling Network optimization and algorithmic methods Machine learning and data-driven approaches