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Field
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of machine learning-based automated ultrasound video analysis models that incorporate temporal reasoning. The research will also include work that aims to understand how human behaviour may change with
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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, Applied Mathematics, or Computational Physics/Chemistry with a strong ML focus. Technical: Deep understanding of Deep Learning (Transformers, GNNs, Auto-encoders). Programming: Proficiency in Python and
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practical, effective, and scalable solutions. The two positions serve complementary but distinct functions within the project. One position focuses on the application of machine learning models for designing
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artificial intelligence tools for medical imaging. The role will support the design of deep learning and predictive models for tumour segmentation, cancer detection, risk prediction, and outcome modelling
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Mobasher. It involves a diverse range of activities including: structural and geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical
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. Candidates with experience in dimension reduction, deep learning, machine learning, modeling neuroimaging data are especially encouraged to apply. Excellent written and communication skills are required
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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strong publication record (first-author papers in high-impact journals preferred). Demonstrated expertise in at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual
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) Experience in genomics, single-cell data, or machine learning (preferred) Why this is exceptional Build next-generation AI models of the human genome Work with one of the richest longitudinal PD datasets