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the other members of the wider project team. To be successful in this position, you will have: PhD qualification in the relevant discipline area, such as computational/theoretical physics or chemistry, or
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electrical and electronic engineering and/or applied mathematics, in particular, in one or more areas of learning, control and optimisation of multiagent systems. Experiences in dealing with complex networks
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field (e.g., Medical Physics, Radiation Therapy, Computer Science, Engineering, Mathematics, Science, Physics, etc). Emerging track record publishing in high quality journals (e.g., Medical Physics, IEEE
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initiatives, securing competitive research funding, and establishing industry links. Qualifications A PhD degree in Computer Science related field is desirable. Please Note: Appointment to this position is
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to develop computational models for engineering decision making, ideally in manufacturing and engineering scenarios. Familiarity with metal additive manufacturing processes and design for additive
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the School of Engineering and contribute to industry-engaged research in construction materials and modelling. This role offers a rare opportunity to work across laboratory experimentation, scaled-up
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research teams, in particular post-doctoral staff working on water-related research funded by the AuSpire program as well as undertake supervision of research students. Extend the research performance and
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(at either level A, B, C and/or D) to support the research fieldwork, project deliverables, data governance, reporting and presentation of research findings for the ‘Measuring Digital Inclusion for First
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in the area of women’s health with a primary focus on endometrial biology, embryo implantation and endometriosis. It is expected that you will work with an increasing degree of autonomy as skills and
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control systems. You will work collaboratively with a multidisciplinary team to advance renewable energy technology through cutting-edge simulation, experimental validation, and system integration. We