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track record in one or more of the following fields: (1) human-computer interaction, collaborative AI, (2) Generative AI/machine learning, (3) interaction design, experimental design, or evaluation
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design and analysis for avionics and data handling systems; Implementation, inference, training, verification and validation of machine learning algorithms on hardware platforms for space or terrestrial
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explainable AI (XAI) methods with user-centred interaction design, combine machine learning with alternative AI methodologies (e.g., rule-based reasoning, knowledge graphs, hybrid approaches where relevant
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propulsion systems, thrusters and components; the design, implementation and verification of electric propulsion test facilities; the maintenance and operation of electric propulsion test facilities; advanced
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The Innovation Management section of the Technology and Operations department at the Rotterdam School of Management, Erasmus University, has re-opened this position. Successful innovation management involves all
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related discipline. A solid background in de novo protein design, protein structure prediction (Rosetta, AlphaFold, …), protein expression, structure elucidation, machine learning, C/C++ and/or Python with
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research at UT. The role blends hands-on lab tasks, digital innovation, and joint research. About the Role As an Researcher in Digital Experimental Mechanics, you will contribute to modernising our
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boundaries of system-level modelling, analysis, design, exploration and synthesis beyond the current state-of-the-art? Or are you curious to learn more about the application of AI for system design? We
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experience in designing, implementing, and evaluating large-scale data-intensive systems or cloud platforms, and enjoy turning ideas into working solutions. You are motivated to teach core computer science
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team at AMOLF, working on fundamental questions on physical self-learning systems as part of the NWO ENW‑M1 project “How do physical learning systems learn?”. The research position is intended to start