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— for example, a landscape that changes with climate, or a local action that produces effects in another part of the system. This PhD project investigates how people learn, update, and use spatial knowledge when
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from machine learning methods to more traditional statistical and econometric techniques. We are driven by science with purpose, pushing the academic frontier by publishing at the highest level in
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design and characterise reconstruction methods—both machine-learning-based and traditional—for the bundles of muons that reach the detectors, and apply them to data and simulations to constrain cosmic-ray
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Want to teach machines the mechanisms behind how the world changes — and build agents that act on them? Join us! World models are controllable, physics- and mechanism-grounded simulators of reality
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the background we imagine would best fit the role. Even if you do not meet all the requirements and feel that you are up for the task, we absolutely want to see your application! The PhD process is a learning
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practical machine learning/artificial intelligence courses and relevant project and thesis experience. You have a keen interest in AI alignment, human-AI interaction, and explainable AI, and enjoy
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machine learning/artificial intelligence courses and relevant project and thesis experience. You have a keen interest in AI alignment, human-AI interaction, and explainable AI, and enjoy collaborating with
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, sensors and instrumentation. Knowledge of one or more of the following areas:Power electronics, Reliability and failure mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI
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interactions between individual cells. Where to apply Website https://www.academictransfer.com/en/jobs/362788/post-doc-learning-interactions-… Requirements Specific Requirements PhD Degree in Physics Preferably
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of years are investigated by combining fieldwork, lab work and computer simulations. By bringing together our fundamental understanding of system Earth and our fresh curiosity we conduct research that is