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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of
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will be on development, implementation and verification of novel algorithms for motion planning and control of autonomous underwater vehicles. You will become part of an inspiring research environment
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degradation modes. Evaluating suitable sensor technologies and data sources for acquiring relevant metrics. Developing tools and algorithms to automatically analyse sensor data, assess asset condition, and
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to have experience with: Phase equilibrium calculation algorithms and their integration into CO2 capture simulation Thermodynamic modeling of phase equilibrium and thermophysical properties related to CO2
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techniques for integrating such solutions into modern SDV middleware. Responsibilities: Conduct research in runtime analysis and reconfiguration of in-vehicle TSN networks. Develop algorithms and prototypes
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solutions and policy impacts. You will design and implement machine-learning algorithms that interact with your simulation framework for scenario discovery, building surrogate models of simulation outputs
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will develop in the position; it is expected that you have previous experience on each of them: Develop and implement CFD models to simulate the behavior of PRO systems. Apply ML algorithms to optimise
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learning representations and improve their interactivity. Make AI explanations more understandable Machine learning algorithms often appear as complex black boxes and much research goes into visualizing