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of epigenetic changes to the DNA as biomarkers of biological age. Validation of Biological Age Biomarkers: validate a short-form of the assessments from Phase 2 and develop an AI model and algorithm-driven
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will design quantum-safe threshold encryption and/or authentication algorithms. The expected outcome is the design of methods, techniques and their software prototype to implement quantum-safe threshold
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research lines. Research line 1: "Digital twinning for 3D network optimization" focuses on developing distributed digital twin architectures and mechanisms for distributed network optimization in networks
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build the sustainable companies and societies of the future. Pervasive and Mobile Computing (PMC) research subject addresses fundamental challenges in distributed systems and mobile networks, enabling
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build the sustainable companies and societies of the future. Pervasive and Mobile Computing (PMC) research subject addresses fundamental challenges in distributed systems and mobile networks, enabling
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, Information Science, Data Science and Artificial Intelligence. We employ over 200 people in four divisions: Artificial Intelligence & Data Science, Algorithms, Interaction, and Software. The atmosphere is collegial and
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, distributed ledgers) Desirable: Experience with generative AI (e.g. LLMs) Interest in Human-Computer Interaction Interest in privacy enhancing technologies (PETs) Other: Experience in presenting or preparing
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spatial distributions of active phases across multiple length scales. The PhD student will collaborate with international partners to study the evolution of crystalline and amorphous domains in the hybrid
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to): Transparency and contestability in AI Accountability in algorithmic infrastructures Technical mechanisms for evaluating and auditing AI systems Governance of open-source and general-purpose AI models Data rights
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application! Your work assignments Our research projects focus on distributed sensing, hardware-efficient signal processing, robustness and resilience, and communication-efficient decentralized machine learning