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Field
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Join TU Delft and help design hydrogen-resistant steels for a sustainable energy future. As a PhD researcher, you will unravel the atomic-scale mechanisms of hydrogen embrittlement in
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” modeling solutions; we are open to and excited about applying all different types of statistical and ML techniques, from linear models to deep learning, depending on what best fits a given problem. The most
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reliable hydrogen-resistant circular steels. In this role, you will develop fundamental insights into the mechanisms governing hydrogen-induced degradation and failure of circular steels. As a PhD researcher
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experimentation and training. Science of Deep Learning: Exploring mechanistic interpretability and understanding the fundamental drivers of model performance at scale. As an early member of this fast-growing team
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working
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) Recognised Researcher (R2) Positions PhD Positions Application Deadline 28 Sep 2026 - 12:00 (Europe/Copenhagen) Country Denmark Type of Contract Temporary Job Status Full-time Is the job funded through the EU
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MSCA Doctoral Network position “Electric solar wind sail mission design” We are now looking for two (2) Doctoral Researchers. This PhD topic is part of the “Electric solar wind sail doctors” (E
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. There is also a need for accounting noise in Deep Learning models and quantifying uncertainty in real-world applications. This PhD studentship (scholarship) leverages large-scale Earth Observation data
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scientific initiative focused on AI-assisted reverse engineering of integrated circuits for hardware assurance and intelligence analysis. The project is conducted within the Deep Learning for Perception and
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data science, biomedical engineering, technical medicine, or a related field. You should have strong programming skills (Python, PyTorch), deep learning knowledge (multimodal learning, longitudinal