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differential equations relevant to computational fluid dynamics. These efforts might include Bayesian physics-informed neural networks and neural operators. Bayesian neural networks for approximating piecewise
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and significant piece of information to the right point of computation (or actuation) at the correct moment in time. To address this challenge, you will focus on developing theoretical and algorithmic
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. Moreover, the network will provide Doctoral candidates with exposure to academic and commercial working environments through a balanced secondment plan and access to a complete training programme
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2026 - 12:00 (UTC) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within
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other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in Electrical Engineering, Computer Science, or Applied Mathematics, with a
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. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning - constitute fundamental scientific domains that act as
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series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related areas, but application to dynamic systems is
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part. Your work may also include teaching or other departmental duties, up to a maximum of 20 percent of full-time. Your qualifications You have have graduated at Master’s level in Computer