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. This project will explore recent advancements in implicit neural representations, which have demonstrated effective neural network activations for computer vision. Our goal is to design new PINN architectures
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One fully funded, full-time PhD position to work with Prof. Mahesh Marina in the Networked Systems Research Group at the School of Informatics, University of Edinburgh. The broad aim
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2025 Apply now Join CropXR as PhD candidate! Develop computational models to translate drought & heat stress responses from a model plant species (Arabidopsis thaliana) to crops. Combine plant biology
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in the activities of the Spatial Heritage Research Group. For teaching duties, the candidate will, in consultation, be involved in the Art History BSc program, in the Architecture History or Art
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History BSc program, in the Architecture History or Art History master programs. Teaching will take place in 2nd, 3rd, and 4th year. The candidate is required to reside in the Netherlands, preferably near
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transport and energy conversion. The incorporation of 2D nanomaterials as sacrificial layers enables precise control over nanochannel architecture, ensuring enhanced selectivity, mechanical robustness, and
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, (parameterised) complexity classes for stochastic computing, and the realisation of probability distributions and computations on them in novel materials and computing architectures. This position contributes
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architectures in the field of computer vision and with training, validating and inference processes in machine learning; Familiarity with generative AI; Curious about mathematics and biology; Excellent
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package within the core research program of CropXR. Our goal is to develop computational methods to translate integrated simulation models of plant responses to stress (drought, heat) from a model plant
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of telecommunications, AI and cloud computing technologies. Key Responsibilities: Use of ML to handle resilience and fail over in 6G architectures Design and develop robust network slicing in 6G architectures