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the Unconventional Communications and Computing Laboratory (UC2), led by Dr Michael T. Barros, which develops modelling and algorithmic methods for networked communication and computation under real-world constraints
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algorithm that allows accurate simulation of fluid transport processes in porous media coupled with chemical reactions (e.g. dissolution and precipitation). The algorithm will be validated firstly against
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. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve
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numerical simulations with optimization algorithms and software tools. · Carry out experimental validation of simulation results Is Your profile described below? Are you our future colleague? Apply now
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homogenisation and energy group structure. Investigate the use of AI/ML algorithms to predict or generate cross sections, enabling deterministic solvers to better capture strong heterogeneities and flux gradients
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statistical methods to large-scale medical datasets (EHR, imaging, genomics, clinical trials). Design algorithms and predictive models to advance diagnostics, medical devices, healthcare access, and
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problems, developing algorithms which have appropriate accuracy, precision, and speed. Write up and present results from own research activity and provide input into the project’s dissemination (technical
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Group (EASE IRTG), Empowering Digital Media (EDM), the Research Training Group HEARAZ , the Research Training Group KD²School (KD²School), π³: Parameter Identification – Analysis, Algorithms
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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-related traits, and heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck of computational load for such a