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, Psychology, Public administration or other social science discipline relevant for this PhD position; can demonstrate scientific research skills; can demonstrate experience with quantitative empirical research
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-free double perovskites Your Profile: Master`s degree in theoretical or computational physics, chemistry, materials science or similar fields Familiarity with atomistic simulations, high-performance
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very latest issues that impact our society
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Your Profile: University degree (Master) in Physics, Chemistry, Computer Science, or related fields, with an overall grade of at least “gut” (or equivalent, e.g. cum laude) Expertise in quantum mechanics
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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and Language Sciences, including our Speech and Language Sciences subject area. The postholder should have a PhD in a relevant discipline e.g. Speech and Language Sciences, Education, Psychology
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at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . WE OFFER The position will be filled for a period of 4 years (2 + 2). The starting date is in October 2025 or as
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within three months of your employment contract start date, and that you participate in an organized doctoral programme throughout the period of employment. As an employee at NTNU, it is important that you
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background in process or chemical engineering Your education must correspond to a five-year Norwegian degree program, where 120 credits are obtained at master's level You must have a strong academic background
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, engineers and PhD candidates. The PhD candidate is expected to develop an advanced engineering noise prediction model for efficient computation of sound propagation in a range-dependent atmosphere where