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device-level properties of amorphous graphene nanoribbons and related structures. This will provide a bridge between atomistic material modelling and experimentally measurable device characteristics
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with deep learning to design van der Waals heterostructures with optimised spin-orbit torque (SOT) efficiency for ultra-low-power memory and computing. Its two pillars are AUTOMATA, an automatic material
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sintering with conventional thermal sintering in terms of electrical conductivity, adhesion, mechanical stability, and substrate compatibility. The candidate will perform material and device characterisation
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models). Expert use of a chemical synthesis laboratory, a wide range of physicochemical and material characterisation is required. Moreover, in vitro and in vivo expertise, will be essential to determine
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guided by theoretical predictions and will combine magnetotransport measurements with material growth and characterization, including MBE and angle-resolved photoemission spectroscopy (ARPES). What We
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students and other researchers on in-situ (S)TEM. Requirements: · Education: PhD in Chemistry, Physics or Material Science, or closely related fields, with a strong focus on nanomaterials and advanced