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covered by the project. The candidate is expected to collaborate with maritime stakeholders and apply the research to relevant industrial or operational cases. The candidate will join a research environment
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international conferences and peer-reviewed journals. You will make supporting scientific contributions to existing research projects complementary to your own doctoral research, in collaboration with other
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collaboration with Lepidoptera genomics initiatives such as Project Psyche , LepEU , and the COST Action 10kLepGenomes , and working within a collaborative and multidisciplinary team. Priority will be given
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a genuine interest in quantitative and mechanistic approaches to cancer biology. Good organisational skills, capacity to work both independently and collaboratively in a multidisciplinary environment
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well as technical expertise in materials simulations, electron–phonon physics, and machine learning. We further appreciate willingness to travel, teach and mentor, collaborate and communicate science. To succeed in
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well as governance challenges and barriers for achieving healthy coastal environments will be identified. Within this project, the PhD student will be involved in collaboration with stakeholders in both the water and
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2 PhD Positions in Computational Modelling of Biological Membranes and Adaptive Functional Materials
How do molecular interactions give rise to biological function? How can materials acquire adaptive behaviour and process information? Although these questions arise in different scientific
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and attention to detail. - Ability to work independently as well as collaboratively within a research team. - Good organisational and time-management skills. - Motivation to learn new experimental and
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with experience in PhD student co-supervision and benefit from full access to state-of-the-art facilities and our strong international network of collaborators. You will be living in Adelaide, which has
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processes. • Apply machine learning techniques and advanced statistical analysis to extract knowledge from complex datasets. • Participate in the evaluation and optimisation of high-performance scientific