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Field
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. The doctoral candidate will play a central role in advancing the system from pulsed to continuous operation. The project is structured around two main objectives. First, the candidate will implement controlled
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systems and control theory. Knowledge of fluid dynamics and related physical modeling. Strong programming skills (e.g. Python, MATLAB, or similar) for data analysis and model development. Ability to work
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adaptation and learning from their experiences. Using a combination of theory, numerical experiments and precision desktop experiments, we will create 3D materials with self-adapting elastic elements
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Memory Control (LMC) research group at the BCBL , Donostia-San Sebastián, Spain. The successful candidate will conduct research in the THALNET project, which aims to comprehensively characterize
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apply them to relevant processes in biomedicine and catalysis. Thanks to the synergy between disciplines as diverse as synthesis, spectroscopy and theory, the PhD students will be able to develop
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, Robotics, Computer Science, Statistics, or related discipline. Strong background in Machine Learning and Control Theory. Demonstrated experience in research projects with industrial partners. Excellent
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research environment. Opportunity to shape next generation control theory within a world leading research group and to collaborate with major energy sector partners. Access to Imperial’s high‑performance
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glasses). Previous exposure to control theory is a plus. You are highly motivated and curious to explore novel research directions in machine learning and theoretical neuroscience. You are ready to engage
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biology, largely due to ongoing discussions about the extent to which it operates in both sexes. Classic theory posits that typically males, but not females, compete for access to mates. Until today
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to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity to contribute to foundational research at the intersection of control theory