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or condensed matter physics, with a willingness to engage in experimental applications. The successful applicant will also be involved in the design, fabrication and measurement of quantum sensors in the Jesper
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). Proficiency in numerical modelling, data analysis and instrument control in languages such as Matlab, Python, C/C++, etc. Familiarity with sensor technologies and applications, machine learning, and electronics
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PhD in Computer Science, Engineering or other Machine Learning-related field. • Programming experience in python, C++ or other relevant language and experience in deep neural networks • Strong
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responsible for the delivery of high-calibre farming systems research, based on a network of already established field trials to evaluate how crop rotation/sequence, time of sowing and nitrogen management
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decision-making. You will play a key role in both supporting and leading the delivery of farming systems research, based on a network of already established field trials to evaluate how crop rotation
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membership is an EMBL Partnership Programme established in 2010, entitled ‘EMBL Australia Partner Laboratory Network’ (PLN). The partnership aims to seed a dynamic, highly collaborative culture across
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focused on understanding and countering harmful narratives and, mis/disinformation, and applying social network analysis. To be successful you will need: PhD in a relevant discipline such as computer
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experienced Postdoctoral Research Fellow B to undertake research in the field of physics-informed neural networks for magnetic sensing. This project combines the strengths of industry and academia to leverage
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an employer contribution of 17% superannuation. 24 Months Fixed-term contract | Part-time or Full-time opportunity. The Australian Plant Phenomics Network (APPN) was established in 2009 under the Australian
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Learning-related field. Programming experience in Matlab, Python, C++ or other relevant language and experience in deep neural networks. Experience and demonstratable knowledge in deep learning, transformer