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Materials Science and engineering or chemistry or related subjects such as corrosion science and engineering. Masters in electronics engineering with prior experience on humidity/corrosion reliability
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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collaborate closely with a postdoctoral researcher responsible for in-situ fatigue testing under corrosive environments, and will participate in the 4D (x,y,z,time) analysis of the AM microstructure and crack
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Job Description Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges? Responsibilities and
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techniques in enzyme kinetics and structure-function analysis Contribute to pioneering research in the green transition and circular economy Be part of a vibrant international research community with excellent
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skills. For further information about the guidelines for PhD studies at UCPH, please refer to: https://phd.ku.dk/english/. For further information about the structure of the PhD programme, please refer to
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for electric machines to address challenges of increasing efficiency, reducing weight, volume, cost, redundancy, and reliability. The candidate will prioritize industry requirements in designing, development and
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to material, cutting tools and parts production. The PhD project will therefore focus on the development of an integrated system combining direct and indirect tool wear monitoring for reliable residual life
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insight into the structure, reactions and intermediates at an atomic level. Our main research fields are: Electrocatalysis for various reactions: water splitting, CO2 reduction ect. Catalytic surfaces
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(or equivalent (120 ECTS points)) in civil or mechanical engineering. Background or strong interest in one or more of the following: Hydrodynamics (wave or coastal) Structural mechanics (nonlinear beam theory