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analyses and machine learning. Some data for the project already exist, but additional data will be collected from behavioural tests on privately owned pet dogs in Sweden and abroad (Europe). Travel and time
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physical interaction necessitate the use of multiple robots to mitigate this issue [1]. Finally, despite the great recent progress in automation, computer science, and machine learning, robots still have
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, Cascais, Riga, Vilnius, Melsungen, Ciampino, Urla and Rhodes. The PhD project will involve: The use of data analytics (statistical models, machine learning, uncertainty quantification) to monitor and
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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work with large-scale behavioural data sets using a range of approaches, including heritability analyses and machine learning. Some data for the project already exist, but additional data will be
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adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural data to decode multisensory information Investigate how neural
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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apply a fast and efficient forest trait mapping and monitoring method based on the Invertible Forest Reflectance Model. A machine learning / deep learning framework will be explored and developed
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monitoring and health monitoring of the different machine components. To this end, multiple dedicated measurement campaigns have been performed throughout the Belgian offshore zone, resulting in a large in
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materials systems at the molecular level with machine learning. The PhD Student will undertake a study analysing mass spectral imaging data streams in real time using machine learning workflows. A pathway for