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Field
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causal inference, integration of heterogeneous data sources, uncertainty quantification Work with a wide range of data types, for example dietary records, biomarkers, omics data, registry data, and sensor
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integrated electrodes. Cell co-culture. Biomolecular characterization. Permeability assays. Epifluorescence and confocal microscopy analysis and image processing. Data acquisition and processing. Sensors and
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be an integral member of the wearables group based at Oxford, led by Aiden Doherty. Our research team has access to world-leading population health data sets with objective wearable sensor measurements
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for next-generation intelligent audio IoT sensors. Your Role and Goals As a Doctoral Researcher, you will: Design energy-efficient digital in-memory computing circuits for on-chip AI inference, spanning both
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· Analyze and characterize heat generation in sodium-ion, and potentially lithium-ion, batteries using the various calorimetric techniques available in our laboratory (heat flux sensors, isothermal
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networks (GNN). Functions to be developed: Build and openly publish a collection of GNNs trained on tasks with known answers (minimum paths, flows, spanning trees), to validate interpretability methods
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the HolistIC project , which develops ultra-low-power system-on-chip designs for next-generation intelligent audio IoT sensors. Your Role and Goals As a Doctoral Researcher, you will: Design energy-efficient
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analysing time-series data from smartphone sensors and usage logs using advanced statistical and machine learning approaches to develop personalised nudging and adaptive interventions. The candidate will also
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Imperial College London is recruiting two Doctoral Candidates (DCs) to work in the Marie Skłodowska-Curie Actions (MSCA) Doctoral Network (DN) "SPARK" (HORIZON-MSCA-2025-DN-01-01), devoted to spatiotemporal
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aircraft wings, led by Airbus and funded by the Aerospace Technology Institute. You will develop strong research, supervision, network, communication and project management skills. You will gain unique