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sustainable manner. Key Responsibilities: Responsible for developing explainable machine learning algorithms for Tunnel Boring Machine (TBM) tunnelling and excavation Developing large language model enhanced
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, characterization, optimization, and autonomous decision-making. Advance Bayesian optimization, active learning, machine learning, scientific models, genetic algorithms, and AI agents in physical laboratory systems
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modeling of novel radiopharmaceuticals, development of advanced image reconstruction algorithms for quantitative PET and PET/MR imaging and the development of deep learning methods to improve quantitative
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develop well-documented open source code implementing algorithms for general use and contribute to the production of scientific reports and publications for high profile journals, including taking
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layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable
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Are you fascinated by application-oriented research in mathematics and eager to work at the interface of numerical optimization, optical design, and uncertainty quantification? In this PhD project
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doctoral network on quantum error correction, as a PhD candidate at TU/e, designing next-generation codes and decoders for real quantum hardware. Information Quantum computers have grown remarkably fast
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Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD in
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, including bioacoustics algorithms developed in the team. What you will do Conducting rigorous research at the intersection of ML and wildlife bioacoustics; Actively participating in regular group and one
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collaborative research in electromagnetic sensor systems, including theoretical analysis, numerical modeling, AI-assisted sensing algorithm development, and experimental validation Qualifications: An earned PhD