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/deploying deep learning models and machine learning applications. Computer skills: Python (PyTorch, TensorFlow), databases (MySQL), 3D Slicer, ITK-SNAP, OpenCarp. Previous experience in research activity in
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skills and experience: Essential criteria PhD in Bioinformatics, Computer Science or other closely related discipline * Experience in deep learning with a focus on predictive modelling for healthcare
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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and PhD students on computational mathematics and AI methodologies. Job Requirements: A PhD degree in Applied Mathematics, Computer Science, Engineering, or a related quantitative field. Deep expertise
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to biological data. PREFERRED QUALIFICATIONS: PhD in Bioinformatics, Computational Biology, Computer Science, or a related field Experience applying deep learning or foundation-model approaches to sequence-based
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. Demonstrating enthusiasm for teaching, the teaching/learning process and for individual students. Applying your skills and experience to your assigned teaching schedule. Participating in and contributing
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with artificial intelligence (machine learning/deep learning) Essential Application/interview Experience with classical image processing techniques (e.g. classification/segmentation/registration
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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. Responsibilities include MRI sequence development, optimisation of diffusion tensor imaging protocols, implementation of AI and deep learning approaches for image reconstruction and denoising, and quantitative
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations