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industrial project, driven by Jungbunzlauer’s commitment to “Naturally Better ingredients”. You will design a scalable manufacturing process for a commercially important ingredient used across food and
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conventional analysis methods struggle. The work is inherently interdisciplinary, combining elements of experimental NMR, chemical kinetics, mathematical modelling, and computational data analysis, and the
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Application deadline: All year round Research theme: Numerical Analysis This 3.5 year PhD project is fully funded and home students, and EU students with settled status, are eligible to apply
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substantial interest in diverse industrial applications due to its potential for fabricating complex metal structures. This opportunity is centred around improving manufacturing productivity with advanced laser
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. This steam can accelerate coating degradation and loss of protection, reducing component lifespan and increasing lifecycle costs. Therefore, coatings with enhanced environmental resistance in hot-steam
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to environmental release and occupational exposure. In sectors such as wastewater treatment, food and beverage processing, and chemical manufacturing, contaminants like PFAS, hydrocarbons, and metal ions accumulate
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, or AI safety conferences. • Experience with Clang, LLVM, program analysis, or compiler technologies. • Familiarity with safety-critical systems or regulatory compliance frameworks. • Knowledge
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complex metal structures. This opportunity is centred around improving manufacturing productivity with advanced laser-matter interactions control and optimisation. The PhD will advance our comprehension
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engineering applications. What you will gain Advanced training in cutting-edge characterisation techniques. Deep expertise in materials science and microstructural analysis. Collaboration with Cummins
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challenges in engineering Desirable: Experience with mathematical modelling, optimisation techniques, or supply chain analysis Background knowledge in bio-based materials, biorefineries, or circular economy