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probability, allow for the application of tools from probability theory to combinatorial problems and motivate the study of the typical properties of various combinatorial models, such as the Erdős–Rényi random
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counterfactual reasoning frameworks that uncover latent mechanisms and enable principled hypothesis testing. Our goal is to advance the theory of representation learning and causal inference in high-dimensional
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of Computer Science and Technology at the University of Cambridge, UK. This position is part of a broader effort to advance fundamental research in classical and quantum complexity theory. The successful candidate will
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
present at top AI/ML venues and validate ideas on real platforms. Mentorship focused on visionary brainstorming, in-depth technical discussions, and collaborative work culture on both theory and systems
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including, but not limited to: Microeconomics theory Applied microeconomics Macroeconomics Econometrics and financial econometrics Political economy Industrial organisation Sports economics. To discuss the
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, combining both accuracy and explainability; (3) extend statistical learning theory to offer theoretical bounds for intrinsically-aligned AI models; (4) employ the newly-developed metrics to train deep neural
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Experience in design and fabrication of devices and/or Soft Robots Experience in Material Testing/Characterization Basic knowledge of Control Theory Application Procedure Informal enquiries are encouraged and
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-resolution metagenomics and work with population genetics theory. This will cumulate in a new understanding of host-microbial interactions through acclimatization and adaptation, that will be validated in
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theories and models. This project aims to develop new insights into how bedrock incision processes interact with geological and climatic factors (i.e. spatially variable uplift, shield building, mega
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generation of researchers to advance rural development theory and practice through entrepreneurship. The network includes five European universities and several non-academic partners, offering