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electronic health records (EHRs) from multiple UK hospital centres using advanced data analytics including machine learning, deep learning, and statistical techniques—with a particular emphasis on deep
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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About the Role The combination of personalised biophysical models and deep learning techniques with a digital twin approach has the potential to generate new treatments for cardiac diseases. Our
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the possibility of yearly renewal subject to funding availability. Responsibilities • Design and implement deep learning architectures, AI agent pipelines, and computer vision algorithms to achieve project goals
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immunology, deep learning models and/or artificial intelligence, as well as hands-on experience with cell culture, cellular/molecular biology, and animal studies. The ideal candidate should be self-motivated
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field. Strong background in machine learning, particularly deep learning and natural language processing. Experience with transformer-based architectures (e.g., BERT, GPT) is highly desirable. Proficiency
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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city models focused on health and environmental infrastructures. Advanced Data Analysis: Advanced skills in machine learning, deep learning, and advanced statistics for processing complex data. Urban
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree (or foreign equivalent) in hand by September 1, 2025. Deep knowledge of functional materials and
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will disrupt today’s most vibrant research frontiers: Embed model-based AI into self-supervised pre-training pipelines Finetune multimodal deep learning models that answer diagnostic questions about X