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research which combined efficient optimization and sequential reliability assessment. The project is funded through an EPSRC call to accelerate research outcomes to achieve a prosperous net-zero and is
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on developing machine learning algorithms to support the use of complex urban simulators in decision-making under uncertainty. This PhD project shifts the focus from optimality to relevance in urban land-use and
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into neural networks. PINNs can model real-world signals with sparse, non-uniform, and noisy data. A key question is determining the optimal method for integrating physical priors into neural networks
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candidate will have a unique opportunity to work closely with researchers and technical experts from CSIRO in a multi-disciplinary team environment. The project will provide numerous research and development
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to accelerate the energy transition in Luxembourg by co-creating an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system, with specific focus
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decision-making for an optimal energy system, with specific focus on cost-effectiveness, emission reduction, and social acceptability. D2ET will develop a comprehensive digital platform for planning energy
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22nd June 2025 Languages English English English The Department of Structural Engineering has a vacancy for a Phd-candidate in characterization and optimization of concrete with low-CO2 binders
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stage IIb-IV Non-Small Cell Lung Cancer (NSCLC) This position will offer numerous opportunities for the right candidate to hone their experimental, project management and scientific writing skills, by
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that balance energy supply and demand, improve electricity grid flexibility, and optimize renewable energy integration. These solutions contribute to climate change mitigation by reducing emissions and to
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transmission. The successful candidate will work on elucidating the regulation of these channels and developing and optimizing gene therapeutic approaches in mouse disease models to prepare for putative future