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, Python, or C++ is required. A strong interest in numerical modelling, sound, signal processing and programming is expected. Additionally, very good command of German and English as well as experience with
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to the project funding agencies. You will have the opportunity to contribute to international forums and apply for new research grants. You will become a part of an ambitious, goal-oriented team while
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programming and statistical knowledge, including machine learning A strong academic record Excellent communication skills with a good command of the English language Beneficial skills for this position include
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wellbeing Develop transferable analytical frameworks and methods that can be applied to other periASTY partner cities and urban contexts Collaborate within an interdisciplinary, international consortium
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modelling (FE) of externally bonded reinforcements and concrete. If possible, experience in programming (python, MatLab). Experience in scientific writing with proven track record. Very good English, German
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-reviewed journals, and participating in international conferences. Your profile You are a highly motivated and dedicated candidate with a PhD in Engineering, Energy Systems, or a related field. You bring
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specifically to XRD-PDF (pair distribution function). The project is linked to crystalline and amorphous alloys, and in an industrial context to process design by establishing the structure-property relationship
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modelling (FE) of externally bonded reinforcements and concrete. If possible, experience in programming (python, MatLab). Experience in scientific writing with proven track record. Very good English, German
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scanning probe microscopy (STM or AFM), ideally with a focus on magnetic nanostructures or spin-resonance techniques Programming skills in Python or similar languages A proactive and collaborative mindset
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international research community starting in October 2025. The ideal candidate possesses a PhD in materials science, physics, chemistry, electrical engineering, mechanical engineering or related fields. Prior