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Field
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learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows for crafting
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deep learning and generative AI approaches, creating synthetic virtual patient cohorts from multimodal datasets. Your work will involve designing advanced algorithms and high-throughput workflows
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proofs obtained via state-of-the-art theorem provers and model-checkers. There will be a need to develop novel synthesis algorithms that can scale. There will be a need to fine-tune foundational models
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other area related to GeoAI Working knowledge of and experience in core machine learning algorithms and specific applications in GeoAI In-depth knowledge of spatial/GIS programming, preferably in Python A
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of such systems, but this is driven from the fundamental theoretical knowledge that underpin them. You will be co-creating new models, algorithms and schemes to advance the field of joint sensing and communications
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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goals As a researcher in this project, you will work on mathematical models for describing the radio environment and to design algorithms for estimating, for example, the location and spectral
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needs. By bridging human-centric innovation, generative algorithms, and sustainability metrics, this project seeks to redefine how novel products and systems are conceived, developed, and evaluated. You
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will develop autonomous on-board guidance algorithms for space missions using open-source numerical solvers for convex optimisation developed at the University of Oxford. The focus will be on designing
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of Oxford. The post is funded by the National Institute for Health and Care Research (NIHR) and is fixed term for 24 months. The researcher will develop multi-sensor 3D reconstruction algorithms to fuse