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Field
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development, human-computer interaction, data analytics, user experience design, remote monitoring systems, energy optimization algorithms, and environmental impact modeling. Human-centric AI-driven sanitation
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input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of
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enhance modern microscopic imaging techniques. Designing setting up and executing the experiments Developing relevant algorithms for experimental data post-processing Mentor and work closely with multiple
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discovery by conducting data-driven experiments and developing machine learning models on diverse biomedical data modalities. Responsibilities Data processing across multiple biomedical domains (image, speech
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conditions, identification of vulnerabilities, and development of resilience enhancement strategies. Contribute to the design, development, and implementation of new models, methods, and algorithms
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problem-solving skills, with a focus on developing innovative solutions for multi-modal LLMs. Self-motivated and able to work independently, managing multiple tasks and projects in a fast-paced environment
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to: Design computer vision algorithms to detect and classify key clinical activities during birth and neonatal resuscitation, including object detection and tracking Develop temporal analysis techniques
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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
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! The Clinic focuses on inflammatory diseases of the central nervous system, especially multiple sclerosis. As part of the Department of Neurology, we study the pathogenesis and treatment of this and other