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of continual graph learning. Continual graph learning studies how graph neural networks can learn from a sequence of evolving tasks, graphs, or distributions while retaining previously acquired knowledges
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distribution contribute to our operations. We have chosen media for this recruitment and therefore we reject contact with advertisers or sellers of recruitment services. URL to this page https://web106
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for 3-year PhD positions in computer science with emphasis on formal methods. Possible topics include (but are not limited to) computational complexity, distributed systems, human factors, logic
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investigate the influence of trajectory accuracy on player perception and engagement within a dedicated laboratory environment. Developing distributed synchronization algorithms: we will design and implement a
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research on post-quantum cryptographic algorithms and protocols, assisting in the design, implementation, testing, and evaluation of secure cryptographic solutions resilient to quantum attacks, and
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- and long-term forecasting. Design hybrid AI architectures and optimize them using advanced techniques such as attention mechanisms and evolutionary algorithms. Implement probabilistic and explainable AI
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count, area distribution, inter-cluster distance) to cellular Potts model (CPM) simulations of spheroid growth and drug responses. Integrating multi-modal data streams, such as live-cell imaging, bulk and
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(e.g. Agentic Reinforcement Learning), evaluation, tool use, agentic harness, or retrieval-augmented systems. Internship/full-time experience from research, engineering, or algorithm-development roles in
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: Semantically Safe Robot Interaction (ID: TUEILSY-PHD20240930-SSR) Distributed Multiagent Planning and Control for Large Aerial Swarms in Changing Environments (ID: TUEILSY-PHD20240930-SAS) Safe Collaborative