31 finite-element-methods Postdoctoral positions at Technical University of Denmark in Denmark
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, EBSD and TEM Mechanical testing, such as tensile and fatigue tests Numerical modelling, such as crystal plasticity finite element modelling Physical Metallurgy, especially on steels Excellent English
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methods such as gas chromatography, Karl-Fischer (for water), titration methods for TAN/CAN, elemental analysis etc. Synthesis and characterization of catalysts. Writing of scientific papers and attending
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to partake the following primary tasks: Develop inspection methods for assessing component condition and material quality Perform testing and analysis to support remanufacturing decisions, including Elemental
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in process and systems development related to laser-based metal AM Familiarity with process sensors for laser-based AM methods Familiarity with design for AM and post-processing techniques Innovative
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maturity and business readiness. In this position, you will work in close collaboration with companies in the sectors of metal products & components, mechanical & electro-mechanical products, electronics
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Job Description We offer an exciting postdoc position dedicated to developing methods for detecting performance disparities in foundation models for fetal ultrasound and understanding what causes
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have a few projects related to our novel method, AQUADA, which uses thermography and computer vision to detect damage in wind turbines and PV panels. We are looking for a new colleague to further develop
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to the air. This project aims at developing analytical methods and capacity to sample and quantify volatile PFAS compounds and air emissions from Danish landfills and treatment plants. The project is carried
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invites applications for a postdoctoral position within a dynamic research team dedicated to understanding and improving taxation behavior through behavioral and experimental methods. This is a unique
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without incurring prohibitive computational costs. Unlike standard sequential sampling methods, the project will explore batch-based active learning, prioritizing diversity and informativeness in data