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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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research on biomedical sensor applications (biomedical sensor interfaces/ integrated mechanical strain sensors) analog/Mixed-Signal Integrated Circuit Design (CMOS, low-power, low-noise design) publication
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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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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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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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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, on fundamental aspects of atomic spectroscopy and quantum physics, and will finally learn how to develop a commercial quantum sensor. Your role: Basic research in the theory and experiments with hot
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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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institutions in five European countries and will be supported by a network of industrial and academic partners. About the position The objective of this project is the development and application of soft 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