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machine learning, and for precision-aware scheduling in distributed systems. They will design, develop and refine a software prototype system for distributed serving of machine learning models that leverage
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online behaviour at scale, across multiple modes of communication (messages, images, video, memes and emojis) that shape participation and meaning in warfare. You will lead and manage a range of individual
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etching. Use ‘zoom’ tomography and imaging to resolve structural variation across scales from 30μm down to 3nm to establish a platform for reverse bottom-up enamel remineralisation. Bottom-up multi-modal 4D
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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or other relevant discipline. Extensive experience with in vivo two-photon calcium imaging. Ability to work independently. Proactive and creative independent thinker. Ability to problem-solve and
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and in combination with existing antifungal drugs. Areas of interest are: development of in vivo imaging assays for understanding efficacy of novel antifungal drugs, determination of resistance
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cohort already registered through our SCAD research portal (scad.lcbru.le.ac.uk ), overseeing consent, data collection, and coordination of clinical imaging, DNA, and biomarker studies. The project offers