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Field
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This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to
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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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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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mechanisms occurring in these materials and their synthesis over all relevant length scales (e.g., cutting-edge ab initio methods, atomistic simulation methods, multi-scale modelling, machine learning) High
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underground conditions. Apply machine learning and AI techniques to enhance model accuracy and optimize design parameters. Contribute to the development of a comprehensive, AI-based design methodology for LUS
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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thermodynamically. Performance design optimization and advanced performance simulation methods will be investigated, and corresponding computer software will be developed. The research will contribute
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
artificial intelligence, machine learning, data analysis, or digital systems would be advantageous but is not essential. We value curiosity, problem-solving ability, and a proactive attitude toward learning
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application! Your work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning