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methodological and interdisciplinary research, and willingness to learn from different fields, including transportation, human-computer interaction, and urban planning. Good communication and writing skills in
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trajectories? Passionate about archival research and oral history? Self-motivated and ready to learn new research skills? The Department of History is looking for two PhD candidates to undertake archival and
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consider novel design principles combining approaches in biosensors, communication systems, and machine learning. Are you motivated to take a step towards a doctorate and open up exciting career
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 4 hours ago
research topic, which is assigned to one of the research areas offered , also allows doctoral students to contribute their own institutional experience. A structured and international learning environment is
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experimental data (from ex-situ and in-situ measurement). Therefore, she/he will develop a way to optimize/guide the experiments trough artificial intelligence approach (machine/deep learning) that he will
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positivamentela experiencia/conocimiento en algunas de las siguientes áreas: lenguajes de programación (Python, JavaScript), técnicas y herramientas software de análisis de datos, machine/deep learning (Pandas
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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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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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analog circuits for implementing ONNs for computing. Modeling, simulate and benchmark different computing tasks such as sensor data processing. Explore ONN implementation topology and its energy efficiency
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create