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Field
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. The growing prevalence of data streams in real-world applications necessitates the development of efficient and adaptive algorithm configuration techniques. Traditional offline optimization approaches struggle
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(entities) given the rules and the rules given the molecules. The aim of this project is to develop a theory and accompanying algorithms to decide if an abstract system can be instantiated by a concrete
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of peptide design and chemistry, computational methods (machine learning, deep learning, genetic algorithms), microbiology, synthetic biology, and related areas essential to developing novel
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alarm signal or take other actions locally. We aim to implement methods for quality control and enhancement of data quality through approaches developed in other PhD projects within SFI Smart Ocean. These
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and grippers offer improved safety and adaptability but introduce new challenges in design and control. Their development is still largely bio-inspired and trial-and-error based. Integrating flight and
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to describe a condition or make a decision to send an alarm signal or take other actions locally. We aim to implement methods for quality control and enhancement of data quality through approaches developed in
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to create secure, autonomous and developable solutions that interact with each other and their surroundings, from the edge to the cloud. Project description For this position, you will be working as part of a
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’ algorithms, however these may not provide physically interpretable results or quantifiable uncertainty. We propose developing data pipelines combining advanced preprocessing techniques, statistical tools, and
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This PhD project looks forward to the emerging generation of practical quantum computers which have sufficient error correction to perform reliable calculations of quantum algorithms which exceed
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operations. The goal of this project is to conceive, develop, and evaluate optimization models and algorithms for increasing the economic efficiency of last-mile delivery operations, including routing