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Field
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developing innovative probabilistic methods to tackle fundamental problems at the intersection of spectral geometry, mathematical physics, and number theory. By exploring the geometry and spatial features
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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About the lab The Laboratory of Causal Systems Immunology combines causal inference, probabilistic AI and large-scale in vivo perturbation experiments to uncover how genes shape immune-cell states
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genomic or molecular datasets Background in Bayesian statistics, causal inference, or probabilistic modelling. Familiarity with modern AI architectures and foundation models. Organizational and social
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fundamental algorithms for producing policies for rich goal structures in MDPs (e.g. risk, temporal logic, or probabilistic objectives), and modelling robot decision problems using MDPs (e.g. human-robot
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develop AI tools for the interrogation of subcellular proteomics data. We are interested to hear from candidates with experience in probabilistic or statistical modelling of complex biological data, such as
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for classifying user activity in office buildings using power consumption data, with a focus on probabilistic approaches such as Gaussian Processes that provide principled uncertainty quantification
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processes under different biological conditions. Apply statistical learning, deep learning and probabilistic modelling approaches to large-scale cancer datasets. Evaluate and benchmark computational methods
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Assistant will support the development and analysis of probabilistic and social choice models, undertake literature reviews and data analysis, and contribute to technical reports and research publications
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, long-horizon human-motion prediction, and planning under uncertainty. The methodological scope includes probabilistic modeling, SLAM/localization, spatio-temporal representations, and decision-making