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Field
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analysing multimodal deep learning models for time-specific cancer risk and time-to-event prediction by integrating imaging with longitudinal Electronic Health Record (EHR) signals. Building scalable
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: PhD degree in Computer Science, Electrical Engineering, or a closely related field Strong research background in computer vision and deep learning Solid experience with multimodal learning, segmentation
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research thrusts within Wu laboratory’s overall programme. We are looking for a researcher with a PhD in engineering, or a related physical science discipline who has prior experience and possess a deep
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at unprecedented resolution. The core innovation of your work will be integrating this data to train deep learning models that predict chromatin accessibility and gene expression patterns. These models will
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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, and rigorously evaluate machine learning and deep learning models (CNNs, DNNs, transformers, graph neural networks, diffusion models, multimodal models, reinforcement learning) as well as software
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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, neuropsychology, neurology, psychiatry, cognitive neuroscience, AI in education, deep learning, biomedical engineering, computer science, and related fields. You will be an integral member of an inter-disciplinary
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environments. • Experience in at least two of: phylodynamics / genomic epidemiology, deep learning for sequence or tabular data, reinforcement learning, spatial modelling, or real-time nowcasting. • Demonstrated
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future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful