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Field
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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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biosynthetic gene clusters and other molecular features relevant to natural product drug discovery Validating machine-learning and deep-learning models to predict the chemical structures and bioactivity
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-supervised learning, and few/zero-shot techniques — the student will adapt models to ecological data. Bayesian deep learning and ensemble methods will be explored for trustworthy uncertainty estimation
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. The PhD position will focus on developing a deep-learning algorithm for analyzing the acquired experimental data. The PhD position will focus on development a comprehensive and AI-driven platform
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of computer vision and machine learning Proficiency in English (oral and written) Experience with Deep Learning is a plus To Apply: Please send a long CV, motivation letter, and academic transcripts to Prof
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, integrative biology approach that utilizes human pluripotent stem cell based model systems, high throughput functional genomic screening and big data based machine learning, bridging the scales from genetics
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, the UK Parliament, and various media outlets including the BBC, and its members have earned numerous international awards. Learn more at https://pinholab.cc.ic.ac.uk . Your goal: Develop advanced numerical
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-class or 2:1 (or international equivalent) Master’s degree in Computer Science, Robotics, Mechatronics or Electronic/Electrical Engineering, or a related field. • Knowledge of machine learning/deep
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | 14 days ago
or equivalent) in Computer Science, Machine Learning or a related discipline. Strong foundation in machine learning / deep learning (theory + hands-on). Evidence may include a thesis, open-source project