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. Design and implement multimodal unlearning techniques to address bias and privacy concerns. Evaluate the generalisability of multimodal learning across different socio-contexts. Validate the proposed
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data are needed to enhance our understanding of sources, pathways and impact of litter. Cefas is developing a visible light (VL) deep learning (DL) algorithm and collected a large 89 litter category
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coupling, applications, and development of regional ocean models. Capitalising and contributing to this effort, this project will Investigate effective downscaling strategies for different regional ocean
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responsibilities include: Development of a flood classification framework for flood type prediction Comparison of different ML algorithms in a sensitivity study Communication with stakeholders Development of open
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using electromagnetic induction (EMI), and ground penetrating radar (GPR) will be combined with soil sensors systems and UAVs at different scales. In particular, we will combine borehole and surface GPR
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to develop Computer Vision algorithm and Interfaces for collaborations with Historians teams. Specifically, the research will focus on structured data with clear repeated patterns, such as characters in a text
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CFD technologies. As the PhD researcher on this project, you will investigate and develop the numerical and algorithmic components needed to make this hybrid high order to low order strategy practical
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algorithmic techniques needed to generate reliable high order meshes for complex, multiscale industrial geometries. You will work within a technically focused research group that maintains regular interaction
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and cellular analysis of the ischemic brain using spatial and single-cell transcriptomics Profile of the graduate The graduate understands key areas of informatics, such as algorithms and software
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successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment