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Field
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assessments (LCA) to quantify the environmental impacts of proposed processes and compare them with conventional alternatives. Develop process models using industry-standard software (e.g., Aspen Plus, HYSYS
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machine learning models with respect to accuracy and uncertainty quantification. - Developing software to implement the goals stated above (most likely in Python). - Disseminating results
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machine learning/AI or cybersecurity, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas. Software programming skills. To be successful
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in using Computational Fluid Dynamics (CFD). Experience simulating complex fluid dynamics processes, preferably multiphase reactive flows, using and especially customising CFD software is an essential
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in using Computational Fluid Dynamics (CFD). Experience simulating complex fluid dynamics processes, preferably multiphase reactive flows, using and especially customising CFD software is an essential
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in accordance with the project deliverables and project strategy. 2. Characterisation of completed models and software and the documentation of research output including analysis and interpretation
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Science Delivery stream of the international SKA Regional Centre Network (SRCNet) by liaising with scientists and translating research needs into system and software requirements. Provide science support to
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scientists and translating research needs into system and software requirements. Provide science support to priority projects, contribute to documentation, and assist with user systems, services, and community
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for drug discovery. Publishing peer-reviewed research and contributing to industrial software tools. About You To be successful in this role, you will have: A PhD in machine learning, computer science
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skills and experience: Essential criteria PhD qualified in relevant subject area Experience working with large datasets e.g. CPRD or similar Experience with relevant statistical software (STATA or R