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is both experimentally realizable and accompanied by a digital thermal model suitable for real-time prediction and control. The two positions are closely coupled. One focuses on physics-driven thermal
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, such as multi-objective optimization, model predictive control, mixed-integer optimization, stochastic optimization, energy management, or production scheduling. Good knowledge of integrated energy
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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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will also be integrated into the wider CENSEMAT research environment at Aarhus University, allowing computed models and predictions to be tested directly against advanced experimental characterisation
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modelling; repeated-measures or longitudinal intervention data; randomized controlled trials or intervention research; open science practices. Teaching The position includes a 20% teaching obligation
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or bacterial or parasitic) as well as their metagenomic sequence datasets available within the EUPAHW- JIP2 - SOA23 consortium and/or in public databases with an overall aim of predicting and preventing