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successful candidate should have excellent study results and a strong background in mathematics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment
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artificial intelligence and control systems. In this project, you will design new algorithms for semantic communication between the cloud and autonomous systems—technology that can transform how robots, drones
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usage, memory and storage demands, and associated carbon emissions while aiming to maintain model quality. Your work will include developing new methodologies and algorithms for resource-efficient
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. Such methods are founded on mathematical models and algorithms from the field of mathematical optimization. The dose planning is based on medical images and a treatment protocol, and it is evaluated by using key
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strong background in mathematics and statistics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience, as well as skilled
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performance should improve over time as more data becomes available. The diagnostic conclusions will be presented to an operator using a combination of AI-based fault isolation algorithms and data-driven
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. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience, as well as skilled at estimating and optimizing complexity of algorithms
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algorithms for resource-efficient learning, for example via data selection and filtering (leveraging that not all data is equally informative). You will also investigate complementary approaches that reduce
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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models and algorithms in Python, with documented experience in PyTorch. The applicant should be knowledgeable with neural networks and furthermore have a strong drive towards performing fundamental