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for Pollinator Monitoring: Train and optimise deep learning models for pollinator detection and classification using annotated image datasets. Post-processing object tracking algorithms will be incorporated
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vision to reduce algorithmic complexity by orders of magnitude, e.g. by tracing paths of trees and extraction from knowledge bases (KBs), as opposed to pure DL Defining specific CSK-premises (in
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through theory and simulation and/or experimental design and testing; developing new image reconstruction algorithms for providing more information with less radiation; and applying our techniques
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, this research project will focus specifically on how multiple grid converter-interfaced assets should be controlled and coordinated in an inertia-less (or almost inertia-less) isolated power network, to ensure
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). Information about the Cresswell group can be found here: https://ccri.at/research-group/cresswell-group/ Your responsibilities Analysis of rich, state-of-the-art datasets: You will analyze multiple data types
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). Information about the Cresswell group can be found here: https://ccri.at/research-group/cresswell-group/ Your responsibilities Analysis of rich, state-of-the-art datasets: You will analyze multiple data types
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aims to provide an integrated assessment of urban vegetation from multiple perspectives, including its social functions and usage requirements, the ecosystem health of green spaces linked to plant
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new generation of perceptual foundation models by contributing advanced perceptual pre-training and fine-tuning algorithms. What you will do You will carry out research and development in the areas
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-tuning algorithms. What you will do You will carry out research and development in the areas of perceptual foundation models, using advances in deep machine learning and computer vision. The goal is to
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. An optimisation tool has been developed that uses a genetic algorithm to optimise the location of BGI taking surface water flood risk reduction and the cost of different interventions into consideration. This PhD