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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
-based digital twin, which can predict the assortment and sequence of two-dimensional all-DNA motifs required for the engineering desired tessellation patterns at the nanometer scale. We will use available
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methods for nuclear fusion, motivated by yield prediction in tritium fuel cycles. The lack of scalable tools necessitates large engineering tolerances, increasing reactor cost. Empirical tests are expensive
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Project details: Surface features are important in additively manufactured parts. While additive manufacturing technology has made great strides in the realisation of complex shapes, topologies and
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25th August 2025 Languages English English English The Department of Energy and Process Engineering has a vacancy for a PhD Candidate in Thermo-Chemical Plastic Recycling Apply for this job See
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both the fundamental physics of electromagnetic materials and practical applications in 6G communications. The PhD is 4 years and funded by DSTL (Defence Science and Technology Laboratory), and you will
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failure before components are built? We invite applications for a fully funded PhD project to develop microstructure-aware simulation models for fatigue and damage prediction in turbine wheels. Working in
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Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well-being of society for a future worth living
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analysis, and high-performance simulations? We offer a challenging PhD position in the Mathematics of Computational Science group to design and analyse novel structure-preserving methods for stochastic
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We invite applications for a fully funded joint PhD studentship between the University of Birmingham (UK) and BAM (Germany), starting in September/October 2025. The project will address critical
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of electrical engineering, Universite de Moncton (Moncton campus) Email: [mohamed.lamine.faycal.bellaredj@umoncton.ca] Context & Motivation: Because of their extreme computational needs where workloads demand