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We are seeking a highly motivated PhD candidate with a strong interest or background in AI as well as in one or more of the following areas: Generative AI, Natural Language Processing, Deep learning
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on: • In-depth expertise in reliability testing of wide bandgap (WBG) technologies • Deep knowledge in in-situ measurement techniques for WBG technologies • You work on developing hybrid prognostic
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centres, we provide an unparalleled learning environment for its 24,000 students and 13,000 staff. At Cambridge, our mission is to contribute to society through world-class education, learning, and research
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internally, so while you learn the intricacies of our industry, you’ll have plenty of opportunities to contribute and directly affect our bottom line within your first few weeks on the team. While interest in
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internally, so while you learn the intricacies of our industry, you’ll have plenty of opportunities to contribute and directly affect our bottom line within your first few weeks on the team. While interest in
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the challenge of time-consuming sideshaft testing. As a key member of the team, you will apply cutting-edge machine learning and deep learning techniques to dramatically reduce testing cycles. You will lead life
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deep learning algorithms by leveraging LLMs and compare them with traditional methods; and 4) develop a set of tools on the project's website that can be used to evaluate lexical complexity, readability
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key regulators of inflammation and tissue remodeling in gut and skin diseases. • Apply and refine AI/ML methods, including deep learning, neural networks, and interpretable models (e.g., SHAP, BioMapAI
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of scientific publications Strong teamwork skills, and ability to work collaboratively in an interdisciplinary environment Good communication skills Strong experience with machine learning and deep learning
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Join a dynamic team of motivated individuals with deep collective experience throughout digital forensics, incident response, investigation, operations, and academic research. We seek individuals