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scalable methods for understanding the inner workings of large language models (LLMs) and LLM agents, with a focus on identifying causal mechanisms underlying tool use, reasoning, and multi-agent
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scalable methods for understanding the inner workings of large language models and developing functionally-grounded steering and control techniques. The successful candidate will contribute to frontier
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scalable methods for understanding the inner workings of large language models and developing functionally-grounded steering and control techniques. The successful candidate will contribute to frontier
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the research in symbolic AI and logic for computational law carried out by the PI and his research groups, centred around the development of a graph-based modelling language, DCR Graphs, for the declarative
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methods, artificial intelligence, and cutting-edge genetic models to support and enhance management and breeding decision-making by dairy farmers. This includes tools for dairy cattle management traits
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control in neuronal synapses You will be working with primary neuron cultures, rodent in vivo models, and patient brain biopsies You will use advanced microscopy for super-resolution and live imaging
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, “Leveraging Algebraic Geometry for High-Precision Fundamental Physics“ (DFF Sapere Aude) led by Associate Professor Matthias Wilhelm and "FunQ: Quantum Programming Beyond the Circuit Model” (DFF Sapere Aude
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with process or reactor modelling Experience with handling pure or complex anaerobic microbial cultures Chemical and microbiological analytical techniques Excellence in both written and spoken English
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: Quantum Programming Beyond the Circuit Model” (DFF Sapere Aude) led by Associate Professor Robin Kaarsgaard Sales. Contact information Further information is available from the Head of the Centre, Professor