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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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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
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PhD Studentship in Aeronautics: Real-time machine learning and optimisation for extreme weather (AE0073) Start Date: Between 1 August 2026 and 1 July 2027 Introduction: Climate change is
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learning, non-Hermitian systems The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for PhD positions to work at the intersection of computational quantum many-body physics, machine
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position in Energy-Efficient Machine Learning for Wearable and Augmented Reality