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Field
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science methodologies (e.g., machine learning). Experience working with large-scale environmental and remotely-sensed datasets, strong proficiency in R and version control tools (such as GitHub), and
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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representation learning. You have strong programming skills, especially in Python, and preferably experience with PyTorch. You have a track record of publishing in top image processing / computer vision journals
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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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Programme Staff Discount Scheme Gym Access (reduced membership cost) On site Facilities: On site Car Parking On site dining facilities On site TFI Bike Sharing Station Eligibility criteria Essential: A PhD
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/264ffa19ca70e3ec41032fe6a4802932b5eda4e6.pdf https://ieeexplore.ieee.org/document/10068193 Job Specifications For PhD applicants: Excellent Master’s degree (or equivalent) in computer science, engineering, or related disciplines (typically
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terminal degree conferred by the Fellowship start date of August 15, 2026. Recent PhDs from any discipline (including but not limited to Africana studies, anthropology, Asian studies, business
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Your Job: As part of an interdisciplinary project team with researchers from bioinformatics you will work on quantum algorithms for drug discovery. Here, the focus lies on machine learning and
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self-adaptation capabilities. Three major challenges have been identified: (P1) modelling uncertain environments where robust, weakly supervised machine learning algorithms can be deployed to irrigate
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication