-
materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
-
., multispectral imaging) and computational tools such as machine learning or AI into research workflows. Strong project management skills, including planning, milestone delivery, and working with industry partners
-
components. The resulting data will be used to train a machine learning (ML) model, enabling automated and efficient beamline alignment. This technology has the potential to significantly enhance
-
materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for
-
the world. Ideal applicants will have a solid background in AI, machine learning, control theory or quantitative finance. Applicants with advanced programming skills (Python/C++); and a desire to publish in