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participate in the Penn Medicine Biobank/Undiagnosed disease program. The Division of Translational Medicine and Human Genetics comprises clinical and research faculty with diverse investigative interest and
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or scholarship responsibilities may include carrying out research projects in collaboration with the TBI center faculty as well as establishing their own independent research program aiming
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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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interfacing of the sensors. The PhD candidate will work at the Integrated Devices and Systems (IDS) group within the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University
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Engineering, materials science or related subject. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website
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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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addressing the behaviour of thin foil materials for aerospace forming applications. The successful candidate will have a first-class or upper second-class honours degree in mechanical engineering or a related
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Collaborative Doctoral Project (PhD Position) - AI-guided design of scaffold-free DNA nanostructures
. Such structures can be used to construct artificial cell mimics and new materials. A scaffold-free DNA tile assembly is a programmable method for the formation of two- and three-dimensional DNA structures which
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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