Topic: uncertainty quantification

Develop methods combining bifurcation analysis, robust optimisation and uncertainty quantification for nonlinear structural dynamics and aeroelastic flutter mitigation in high-altitude, long-endurance drones. The work includes modelling uncertain nonlinear systems, numerical implementation and comparative evaluation of robust bifurcation-diagram optimisation. The ANR FlexHALE appointment lasts 18 months in Inria Saclay’s PLATON team, with a start no later than January 2027 and approximate gross pay of €2,700 per month.

Develop Bayesian calibration methods that explicitly represent model error in structural dynamics. The PLATON team will compare informative calibration quantities, implement and benchmark algorithms, and accelerate inference with surrogate models. The work aims to improve finite-element predictions under uncertain material, boundary and loading conditions. Applicants need a relevant doctorate and scientific-computing expertise, with experience in structural dynamics, model calibration or uncertainty quantification. Appointment: 12 months; advertised start 2027-01-01. 2788€ gross/month.

Develop computational methods for biological digital twins, including parameter estimation, uncertainty quantification, Bayesian inference and physics-informed neural networks. Build scalable simulation software and apply it with experimental and clinical collaborators to development and disease models.

UKRI and EPSRC are funding speculative, high-risk fundamental research with the potential to produce a step change in the explainability of future AI systems. The call welcomes work on mechanistic interpretability, output reasoning, process examination, imposed explainability, uncertainty quantification, and novel approaches beyond those examples.

A common approach to analyzing spike train data in stimulus-response experiments is to estimate spike rates relative to the stimulus onset. These experiments typically involve collecting measurements from the same subject across several trials and sessions, and performing the same experiment across multiple subjects. Accounting for these sources of variability is important to accurately estimate the population response. However, current spike rate estimation methods only allow for estimating trial-level, session-level or group-level spike rates, neglecting the variability between levels. We de

Neural decoding is essential for understanding the association between neural activity and behavior. A prerequisite for most decoding methods is spike sorting, the assignment of action potentials (or spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-channel-count probes like Neuropixels (NP) and extracellular analysis pipelines allow for extracting a rich set of spike features to direc