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Location: Central Cambridge We are seeking applications for a PhD position in AI in proteomics as part of the Horizon Europe MSCA Doctoral Network, ProtAIomics. This brings together laboratories
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networks of nodes and parameters. This practice-based PhD investigates how AI can assist at the level of tool production by drafting procedural networks that the experienced technical artist can inspect
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noisy, partially mediated observations. These models will incorporate biologically informed structure, including protein-protein interaction networks derived from data-driven sources such as protein
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal inference and time
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enjoys a wide network of strong international collaborators all around the world, for example at the University of Oxford, the University of Melbourne, and the University of California, Los Angeles. We
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mathematical background Core skills: Probability and statistics. Estimation, Bayesian inference, uncertainty quantification and calibration (proper scoring rules, reliability diagrams, ECE), experiment design
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data and data integration, especially in molecular medicine; mathematical modelling of cancer; probabilistic modelling and Bayesian inference, stochastic algorithms and simulation-based inference; causal
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
modeled using an evolution of this algorithm, which replaces the SVRs with a multilayer neural network [Ribes et al., 2024]. In 2025, it was used in an inverse-computation context using Bayesian inversion
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with researchers and practitioners through the aiD network and related national initiatives. Contribute to the scientific development and collaborative activities of the Data Science research group