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(preferably in Python, including deep learning frameworks such as PyTorch); affinity with medical image analysis and computational modeling; experience with neural networks for image analysis, generative
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, generative AI, and blood flow modeling. You will: develop deep generative models (such as latent diffusion models, implicit neural representations, and flow matching) for uncertainty-aware 3D reconstruction
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metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a
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machine-learning approaches. Established markers such as neurofilament light chain (NfL) and GFAP will provide a biological reference point for identifying disease-specific biomarkers. A central part of the
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evaluation of pharmacological therapies under the supervision of Dr Rogier Min. At the National Research Council of Italy (CNR) in Cagliari, you will learn computational chemistry approaches to identify drug
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analysis equipment and software and learn how ideas emerging from academic research can be developed towards commercial application. You will also undertake a secondment at the University of Liverpool’s
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questions. The candidate should be organized, curious, analytically rigorous and comfortable learning unfamiliar methods. Good written and spoken English is essential. Research motivation: a clear interest in