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on solving challenges at the interface of Synthetic Biology, Control Engineering, AI, and Robotics, and their application to global challenges across biotechnology. The approaches taken will depend
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of Klebsiella, a clinically important pathogen. Combining eco-evolutionary theory, lab experiments, and genomic sequencing, you will help build and validate a model predicting how antibiotic selection shifts with
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Engineering, Control Engineering, Mechatronics, Robotics, or a related discipline with a strong focus on dynamic systems. Strong background in state-space modelling, estimation theory, and control systems. Good
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statistical methods for adaptive learning, sequential decision-making, and control, motivated by applications in public health and epidemiology. The project will focus on methodological advances in
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the work aligns with your goals and aspirations. For more details see https://binyshlab.com/positions/ What will you do? Conventional robotic bodies rely on computationally intensive centralized control and
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on information theory, machine learning, and control to analyse how local variability, sensor drift, and platform differences affect both global model performance and human supervisory factors such as workload and
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slowly, or act quickly but lack robust planning. Frontier models typically depend on heavy compute, cloud inference or controlled settings, limiting real-world use under compute, latency and energy
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exponentially accurate approximation or controls in PDEs. Gevrey regularity allows one to go beyond the smooth theory and obtain quantitative remainder estimates, while retaining useful tools such as cutoffs and
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gel-like structure called the third phase. Third phase formation is, however, a processing disaster and must at all costs be avoided. An ability to predict and control third-phase formation is essential
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Approximation calculations, whose direct use in Bayesian parameter estimation is currently computationally prohibitive. By providing a fast and statistically controlled surrogate for these calculations