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Field
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work on plant genetics, growth strategies, and crop management with the aim of achieving optimal crop performance by integrating plant, environment, and management. The position offers close
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twins, optimization, and control. In this PhD project, you will develop a new systems and control theory for learned operators, bridging modern scientific machine learning with classical control theory
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such as Bayesian modelling and optimal control theory. Using state-of-the-art methods – including virtual reality, wearable sensing, motion platforms and advanced data analytics – you will place particular
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that could be highly valuable, for instance to consumers and patients wanting to monitor and optimize their health in a home setting, or to workers operating outside the reach of regular medical care and
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incentives, network constraints, and regulatory decisions in shaping collective outcomes. Identifying optimal designs and interventions is further complicated by multiple, often competing, objectives
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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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selection and validation, supervised and unsupervised learning, optimization techniques, (deep) neural networks, probabilistic methods and statistics, data visualization, natural language processing
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networking scenarios. You will analyze the combined FSO-RF-Fiber channel and develop accurate but sufficiently lightweight channel models and come up with jointly optimized schemes for such hybrid links and
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confidence in the measurements and establish traceable validation routes. Optimize the methods for realistic converter operating conditions and communicate practical guidance to academic and industrial users
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retrieve shapes, overlay errors, and other geometrical parameters of the target using methods ranging from local and global optimizers to priors and neural networks developed by partners in the project. Job