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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Infrastructure? No Offer Description Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
Develop machine learning models to detect early signs of abrupt shift towards clean energy technologies and make climate action adaptive to this information. Job description Positive tipping points
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look forward to receiving your application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems
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opportunity to develop deep expertise in robotics, machine learning, and control. WASP is a major national initiative for strategically motivated basic research, education and faculty recruitment. It is by far
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: Data Mining Machine Learning Bioinformatics The successful candidate will contribute to advancing state-of-the-art in data mining and machine learning research with applications in computational biology
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PhD Scholarship Develop multimodal machine learning models to predict glioblastoma treatment outcomes using imaging and clinical data. Work with real-world data from John Hunter Hospital in a
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look forward to receiving your application! We are looking for a PhD student in AI and machine learning with a focus on generative machine learning methods for cyber security applications. Your work
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industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data
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application! We are looking for a PhD student in AI and machine learning with a focus on generative machine learning methods for cyber security applications. Your work assignments The primary focus of this PhD
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PhD Studentship in Aeronautics: How offshore wind farms and clouds interact: Maximising performance with scientific machine learning (AE0078) Start: Between 1 August 2026 and 1 July 2027