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have or is finishing a bachelor's degree in chemistry, chemical engineering, material science or engineering or relevant fields. Preference will be given to an honour’s student or a master's degree by
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(or equivalent) in a biomedical science. Experience in neuroscience and/or immunology is desirable. Project key words Retinal imaging, data-analytics, computer vision, big data Funding The studentship, funded by
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for sustainable production of food by applying your microengineering and material science skills? Here, we can offer you a unique opportunity to do exactly that in a dynamic research environment. In the SOLARSPOON
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Computation and Data Driven Design of Materials for Onboard Ammonia Cracking This exciting opportunity is based within the Advanced Materials Research Group at the Faculty of Engineering which
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potential. Our research focuses on Materials physics, Quantum technology, Soft & living matter, and Advanced energy solutions. Topics extend from fundamental research to important applications. We educate
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
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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
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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
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of the Open Competition Domain Science-M programme (Twenty-one innovative research projects awarded through Open Competition Domain Science-M programme | NWO - https://www.nwo.nl/en/news/twenty-one-innovative
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publications and project reports Support in establishing the institute Your Profile: Master`s degree in physics, chemistry, materials science, chemical engineering, or a related discipline Extensive knowledge