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: www.zhulab.com The candidates will focus on one or two of the following projects: Developing predictive toxicity models for assessing new environmental pollutants, nanoplastics, and environmental mixtures
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, generating process, material, and structural data to support model development, validation, and process consolidation. Develop and apply physics-based and data-driven modeling frameworks to describe, predict
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20th October 2026 Languages English English English The Department of Biology has a vacancy for a PhD Candidate in Modelling Marine Larval Dispersal and Spatial Population Genetics and Demography
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: to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data and crop models, we aim to enable more precise and
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of dynamical systems, control of complex and uncertain systems, motion control for high-tech systems, model predictive, networked, supervisory, neuromorphic, and learning-based control, cyber-physical systems
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | about 2 months ago
institutes, with the student spending substantial time at each site to leverage their complementary experimental platforms and expertise. The PhD candidate will focus on developing novel control and modeling
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Strong background in control, robotics and machine learning, and experience in areas like model predictive control, adaptive control, reinforcement learning, robot perception or manipulation. Programming
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implementation or comparable tools. Experience with one or more relevant methods, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy
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to increase resource use efficiency. However, managing and optimizing these new cultivation systems requires crop models that can predict plant growth, crop yield and resource use under highly dynamic
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: to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data and crop models, we aim to enable more precise and