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science (for example, but not limited to, data science, machine learning, statistics, computer science, mathematics, etc.) acceptance by a PhD supervisor with examination admission for PDS and formation
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student projects and BSc/MSc theses Your Profile: Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning
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performance in fuel cell (biogas) and co-electrolysis applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under
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applications. To achieve this, you will employ computational fluid dynamics (CFD) and machine learning (ML) to investigate degradation mechanisms under various operating conditions and develop strategies
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, experimentally grounded workflow for rapid microstructure-property optimization in steels. The PhD student will play a central role in this interdisciplinary initiative. They will: Develop and apply machine
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 11 hours ago
Degree Doctor of Philosophy (PhD) in Heritage Studies Course location Cottbus Teaching language English Languages All courses are taught in English. The dissertation is written in English, and the
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PhD Position in Theoretical Algorithms or Graph and Network Visualization - Promotionsstelle (m/w/d)
students with strong theoretical foundations and a desire to contribute to fundamental algorithmic research. Our group works at the intersection of algorithms, machine learning, and interactive visual
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of embedded machine learning, neuromorphic hardware and deep learning accelerators. Want to get more information? Click here. What you will do Responsible for RTL design (VHDL, Verilog) of digital blocks and
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Machine Learning Cluster of Excellence TERRA CIN LEAD Graduate School & Research Network Collaborative Research Centers Transregional Collaborative Research Centers (CRC-TRRs) DFG Research Units Research
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and machine learning to establish a modeling framework that uses omic data for providing effective degradation rates of biomolecules and predictions of their impact on soil organic matter turnover