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an asset. You are self-motivated, organized, independent and have a careful way of working. What you can expect You will gain hands-on experience in sensor development and characterization. You will be
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architectures, and demonstration of showcase applications, like light emitters, light sensors, supercapacitors, and batteries. Research and training tasks will be carried out by a collaborative and
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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization methods, machine learning algorithms, and
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semester in EUR None Combined Master's degree / PhD programme No Joint degree / double degree programme No Description/content Lately, low-cost sensor devices have gained significant computing capabilities
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electrolysis. As an MSc student, you will design and implement a suite of AI “agents” (autoencoders, statistical models, LSTMs and LLM-based rule engines) that process historical and live sensor data (voltage
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wide range of markets such as automotive, industrial and aerospace. Our interdisciplinary department "Microdisplays and Sensors" deals with the development and fabrication of microdisplays and sensors
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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algorithms in the field of welding technology. Joining three different metal sheets using resistance spot welding (RSW) presents researchers with challenges. We are tackling these as part of a public research