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to develop learning-based model predictive control (MPC) algorithms for multi-agent multirotor drone navigation around vessels in maritime environments. The role will focus on integrating multirotor
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power management • predictive power management from circuits to voltage-aware model optimization and test chip demonstration • predictive power management from circuits to voltage and temperature
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-aware learning and experimental validation. Key Responsibilities: Develop AI-guided experimentation methods integrating data, predictive modelling, uncertainty and validation. Build structured
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, artificial intelligence, and predictive modelling, CCEH develops evidence-based solutions to support climate risk assessment, disaster preparedness, adaptation planning, and more climate-resilient
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data-driven modeling. • Experience in model predictive control or reinforcement learning is preferred. • Good publication record in energy-efficient cooling or AI-assisted energy systems is desirable. Req ID: 33224 Apply now
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pharmacogenomic and patient-derived datasets to build deployable predictive biomarker panels offers additional scope. Work that matters The Integrative Network Modelling Group within the Computational Systems
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clinically relevant predictive models. The lab also maintains extensive collaborations with experimental and computational scientists across multiple academic, industry, and government partners. Trainees
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(BAC) system, Artificial Intelligence (AI) & Machine-Learning (ML) applications. Good written and oral communication skills Proficiency in power system modelling, advanced control theory (e.g., model
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validate data-driven methods that combine production data, predictive and state-estimation models, uncertainty-aware learning and constrained optimization to improve process consistency under process
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ARC Training Centre in Predictive Breeding for Agricultural Futures, you will work with BASF and other research partners to explore Genotype-by-Environment-by-Management interactions and help refine