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The PhD will develop AI methods and approaches to enable accurate characterization of metal scrap, for more efficient metal recovery and recycling Job description The volume of available metal scrap
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and optimizing post-plasma catalysis strategies, we want to create an energy-efficient and scalable method that reduces greenhouse gas emissions and supports the transition to a carbon-neutral chemical
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and quantitative sustainability assessment methods, including Life Cycle Assessment (LCA), Energy System Analysis (ESA), and socio-economic modeling. The candidate will be expected to contribute
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essential for transitioning to a carbon-neutral and energy-efficient society. Methane, a key component of natural gas and a by-product of numerous industrial processes such as naphtha cracking, presents a
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by-product of numerous industrial processes such as naphtha cracking, presents a compelling opportunity. Current practices, such as combusting methane to sustain energy-intensive operations
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the UK Lead on participant recruitment and conduct qualitative research using participatory methods and semi-structured interviews Analyse data, contribute to co-design workshops, write reports, and
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, such as financial reporting, management accounting, and sustainability, using state-of-the-art experimental, analytical, and archival methods. Our research group is highly ranked, has a strong international
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learning, e.g. using JAX, based on numerical models such as Higher Order Spectral method, mcsimpy, etc. Collect real metocean data from relevant online databases, datastreams such as from R/V Gunnerus, and
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. using JAX, based on numerical models such as Higher Order Spectral method, mcsimpy, etc. Collect real metocean data from relevant online databases, datastreams such as from R/V Gunnerus, and experimental
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necessary to consider also the control methods through which it is possible to increase both machine efficiency and controller efficiency. The overall goal of the project is to develop the control methodology