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biopsies and advanced, preclinical models. A combination of wet-lab and computational biology, close ties to the clinic, and a wonderful team of early career scientists give us the agility and expertise
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highly motivated doctoral student to join an ambitious project aimed at building machine and deep learning models to study the genetics of human disease. Funded as part of the Helmholtz AI program, the
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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histological validation within murine cancer models. We offer an association with the Collaborative Research Centre 1450 “Insight – Multiscale imaging of organ-specific inflammation” graduate school
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biological data, development of deep learning and large language models for biological discovery or graph-based methods for molecular and cellular networks. The technological foundation further consists
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nurseries, understanding how species are affected by different forest management practices, and modelling how conservation and timber production can be combined over time. The PhD students will be employed by
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models at many scales including electron dynamics, soft-matter physics, materials-biomolecules interaction and device physics exploiting the collaboration with a multidisciplinary team of theoreticians and
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Professor Nicola Gaston (University of Auckland) ‘Modelling approaches for percolating nanoparticle/nanotube networks’ with Dr Elke Pahl (University of Auckland) ‘Plasmonically-enhanced perovskite devices