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systems with "self-diagnosis" and "self-healing" capabilities. By integrating federated learning, graph neural networks, and blockchain technology, we will develop a framework that moves beyond static
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, which frequently produces designs that are suboptimal when subjected to real-world dynamic environments. Although a handful of advanced, high-fidelity solvers have been developed to tackle this issue
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both individual decision-making and multi-agent cooperation. The student will investigate two complementary directions: ToM-Enhanced Decision-Making for Autonomous Agents: Develop decision-making
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size, weight, power and cost constraints, accurate relative positioning and motion knowledge across platforms, coordinated data acquisition strategies, and the development of SAR image formation
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are excited to push the boundaries of responsible AI. Learn more about the lab's work at: https://martinpawelczyk.github.io/ . Tasks and Responsibilities Develop machine learning methods and tools with a
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. The studentship will start on 1st October 2026. Project Description Project Aim To develop and validate an AI co-pilot software system integrating multi-modal radiomics data to enhance cancer detection speed and
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candidate will develop and conduct empirical research examining human interaction with algorithmic decision-support systems in clinical contexts. The precise focus of the project will be refined
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network integration for emerging low-energy opto-electronic AI systems and beyond. The challenge: Machine learning and neural networks are super-charging the complexity of problems that computer algorithms
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains from