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Experience with using inference/machine learning tools and basic programming is a plus As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe
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in their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ You will be hosted in the Process Modelling, Automation and
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technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their applications in
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-flexible technologies, and integrate machine learning-driven digital twins for predictive combustion modeling. The research program will cover a wide range of e-fuels (H₂, NH₃, CH₃OH, DME, OME) and their
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theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be opportunities to present at leading
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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researchers in soft robotics, control theory, and machine learning. They will have access to a fully equipped lab and benefit from collaborations within the ERC team and across TU Delft. There will be
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) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness of the assessment. All components assembled
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(e.g., Kalman Filter) or Machine Learning models. These tools will be integrated with physics-based models of environmental loading (waves and wind) to enhance the accuracy and robustness