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simulation models and enriched by operational equipment performance data. To this end, physics informed machine learning techniques will be used to bring model data and real data together in a Digital Twin
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Challenge: Analyse properties of biological systems Change: Develop novel control theory and machine learning methods to study natural systems and their robustness Impact: Produce new intelligent
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-driven control algorithms, biomechanical modelling, system identification, machine learning, control theory. Prior experimental experience on human body dynamics and motion comfort. A strong academit track
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developers of JetBrains. More information is available here: https://lp.jetbrains.com/research/ai-for-se/. We are looking for a candidate who has: A Master’s degree in computer science, machine learning
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learning beyond your comfort zone. Beyond your electrical engineering / computer architecture background, you have a keen interest to expand your expertise in the areas of machine learning and neuroscience
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related field. Proficiency with machine learning methods and corresponding software packages is a plus. Experience with ultrasonic welding is a plus. Fluency in English and proven academic writing skills
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a PhD in aerospace engineering, applied mathematics, mechanical engineering or other related fields. Affinity with physics-informed machine learning, computational VVUQ (verification, validation, and
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shall focus on state-of-the-art machine learning techniques, that can find patterns within vast amounts of data, to refine the estimation of flight trajectories and fuel consumption. Additionally, you
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participants, machine learning. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities (based on scale 10: €3.226,00 - €5.090,00). Depending on your knowledge and
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The mission of TU Delft's Quantum and Computer Engineering (QCE) department is to invent, design, prototype and demonstrate the potential of disruptive computing engines by harnessing unique