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Field
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foundational methods for integrating single-cell and clinical transcriptomes; and train, fine-tune, and validate deep learning models using multi-omics and imaging data to predict clinical outcomes such as
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, multimodal, and agentic AI, as well as foundation models, with a focus on geometric deep learning, large-scale knowledge graphs, and large language models. Fellows will also have the opportunity to apply
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minimum qualifications at the time of hire. PhD in computer science, data science, or related discipline Track record of publications in Artificial Intelligence and Deep Learning in peer-reviewed
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learning (“buddying”) Both roles will contribute to a developing cross-disciplinary Deep End research community Developing Skills & Expertise: Develop academic skills around research from design to delivery
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processes under different biological conditions. Apply statistical learning, deep learning and probabilistic modelling approaches to large-scale cancer datasets. Evaluate and benchmark computational methods
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) years in Computer Science, Data Science, Computational Engineering, Bioinformatics, Computational Social Science, or another highly quantitative field with substantial emphasis on machine learning, deep
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The Research Fellow (RF) will conduct research in the field of coastal dynamics, with a focus on the development and application of machine-learning-enhanced coastal models. The project aims
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the correctness, robustness and reliability of deep neural networks and AI-enabled software systems. Job Responsibilities: Conduct research in adversarial machine learning, AI security and the robustness of deep
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materials discovery with cutting-edge high-throughput platforms, robotics, machine learning, and autonomous experimental workflows. Access World-Class Facilities: Based in the Department of Chemical and
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record of publications in reputable peer-reviewed journals in maritime transport, logistics management, machine learning, deep learning, or optimization Proficient in both written and spoken