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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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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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-predictive control, etc.), data-driven modelling and control methods (e.g., reinforcement learning, transfer learning, etc.) Proficiency in programming tools and languages, e.g., MatLab, Python
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innovative computational methods to analyse large-scale multi-omic datasets, identify mutational patterns across cancer genomes, and build predictive models that improve our understanding of tumour
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datasets to develop predictive models of therapy response and identify clinically actionable biomarkers. Located in Boston and the surrounding communities, Dana-Farber Cancer Institute is a leader in life
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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
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, Aspergillus and Yarrowia, characterizing more than 500 prototypes through multi-omics, sensory analysis, life-cycle assessment and safety testing. Predictive models for flavor, texture, nutrition and process
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, Aspergillus and Yarrowia, characterizing more than 500 prototypes through multi-omics, sensory analysis, life-cycle assessment and safety testing. Predictive models for flavor, texture, nutrition and
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; advanced process control (APC); model predictive control (MPC); digital twins and real-time process monitoring and control; process analytical technology (PAT); process intensification and sustainability
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grassland ecosystems, b) integrate remote sensing data (e.g., NDVI, EVI, land surface temperature) with eddy flux measurements to scale up findings and develop predictive models of water use efficiency