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of antibiotic resistance. You will build generative protein models to predict plausible future resistance mutations, use these models to guide high-throughput experimental screens of millions of enzyme variants
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models and simulations that deepen the understanding of the underlying physics involved. The ultimate goal is to create predictive, physics-based models that optimise and control pharmaceutical
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systems, including renewable energy sources and energy storage systems. Development of predictive models and soft sensors for monitoring the technical condition and operational parameters of energy
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Experience with life-history, developmental biology, or behavioural ecology experiments in small invertebrates Experience linking empirical data to modelling or theory Experience with controlled laboratory
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Mathematics (LSM) across 20+ Singapore primary schools. Using person-centred analytic models, the study identifies heterogeneous learner subgroups across cognitive, literacy, mathematical, and socio-emotional
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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, Purpose of the Job We are looking for one Research Associate to work on the development, implementation and testing of predictive control algorithms for the optimal coordination of available multi-energy
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mangament in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. Scientific context Many engineering and
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. Our dedicated and compassionate faculty and staff are driven by a common mission: Contribute to innovative approaches in predicting, preventing, and curing diseases, shaping the future of medicine
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. Develop AI and machine learning models for recycling process prediction and decision support, such as forecasting metal recovery, impurity levels, energy use, and emissions. Develop optimization and control