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using methods such as Dynamic Mode Decomposition with control (DMDc). You will also assist in the development of predictive control approaches based on reduced-order models, and contribute to workflow
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-based models that optimise and control pharmaceutical manufacturing processes effectively. Your main responsibility is to develop and enhance discrete element models (DEM), integrating physics-based
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shaping will be central to the study. The numerical model will be based on the boundary element method (BEM) and semi-analytical approaches developed at I2M. The experimental proof-of-concept will leverage
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of the control law. This will alleviate the modelling complexities and the online computational requirements of the control algorithms and provide them with learning, self-regulating and adaptive capabilities
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 5 days ago
(Verhaeghe 2021); or even functional test migrations (Hlad and Verhaeghe 2025). Challenges The objective of this thesis is to extract and model the control and data flows observed during the execution of a
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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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, or at a specific controlled test-site such as NLR’s (MITC) “DigiCity” urban city mock-up initiative, or a similar initiative near Schiphol Airport. As for the subjective comparison, VR experiences based
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, and machine learning models for functional genomics research in mycobacteria. Responsibilities Responsibilities include: Develop and maintain Django-based web applications and databases for sharing
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-based techniques (e.g., deep neural networks) will be used to automatically learn the system dynamics and the modelling errors, as well as to obtain an automatic tuning of the cost parameters/constraints
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behaviours of multi-agent systems in response to changing internal states and external environmental conditions. Both traditional model-based approaches and modern learning-based control techniques will be