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Postdoc (f/m/d) Mechanical Characterization and Materials Assessment for Nuclear Applications / P...
to standard fracture mechanics parameters # Systematic correlation of fractographic observations with microstructural features and macromechanical test results # Finite element simulation of mechanical tests
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Helmholtz-Zentrum Dresden-Rossendorf - HZDR - Helmholtz Association | Dresden, Sachsen | Germany | 2 months ago
observations with microstructural features and macromechanical test results # Finite element simulation of mechanical tests, in particular for stress analysis at fracture initiation sites in the context of local
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive
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heavy-metal-based quantum dots. We will explore the potential of these sustainable luminescent nanomaterials as color-converting layers in light-emitting devices and in other advanced optoelectronic
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SPECIFICS Join a Dynamic Team Focused on Foundation AI modeling and Physics-Informed Machine Learning as a Postdoctoral Researcher at The Pennsylvania State University. The Pennsylvania State University is
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any insect system. The position is primarily laboratory-based, with a substantial computational component, and will be carried out as part of a collaborative research team. Candidates from any relevant
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calculations Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed before the position begins. Additional information
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in the area of probabilistic methods in machine learning. These include applications of Random Matrix theory and Gaussian processes to Deep Neural Nets. Teaching may be required. Education and
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transition-metal complexes. The project will involve the reaction development, mechanistic studies, and synthetic applications. The postdoctoral associate will perform parallel reaction condition screening
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autonomous, closed-loop (“self-driving”) laboratory workflows. The role integrates catalyst synthesis, high-throughput reactor testing, and in situ/operando characterization with machine-learning and agentic