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- Chalmers University of Technology
- Constructor Knowledge Labs gGmbH
- Inria, the French national research institute for the digital sciences
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chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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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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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
approach is based on neural techniques known as SBI (Simulation-Based Inference) [Cranmer et al., 2020]. SBI enables the resolution of inverse problems using generative AI methods and Bayesian statistics
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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for Bayesian/statistical modelling with uncertainty analysis or programming skills in Python, MATLAB or equivalent and a background in hydraulic, kinetic or systems models. Also highly desirable to have
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criteria Machine Learning Expertise: A robust foundation in probabilistic modeling, Bayesian inference, deep learning, and/or anomaly detection Modeling & Simulation Experience: Familiarity with Building