Probability Theory workshops

This school and workshop joins physics, applied mathematics and ecology to understand ecological dynamics and biodiversity across scales. Students learn to connect empirical data with individual-based models, differential equations, stochastic dynamics and complex interaction networks. The curriculum addresses marine species fluctuations, metapopulations, microbial ecology, animal movement, spatial scaling of plant communities, habitat fragmentation and evolutionary rescue. The January 23–24 workshop includes invited and contributed talks, posters, and discussion of theoretical ecology's future in South America. Master's and doctoral students are the principal school audience; advanced undergraduates and postdoctoral researchers may also apply. There is no registration fee, and limited travel and local support is available. School applications closed on 25 October 2026; the workshop application deadline is 20 November 2026.

This course introduces Markov and latent-state models for longitudinal observations. Participants learn model specification, estimation and interpretation, including covariate extensions and responses that are continuous, categorical or incomplete. Worked examples and exercises use the LMest package in R and connect the statistical assumptions with applied research questions. It is designed for academic and industry participants with little prior probability training; basic R familiarity is helpful. The four online sessions run 14:00–17:00 Berlin local time. The listed package fee is €420.

Marking a century of Richardson’s work on turbulent dispersion, this programme compares Lagrangian and Eulerian approaches, stochastic and deterministic models, particle dynamics and continuum descriptions. Topics include multiparticle dispersion, experimental particle tracking, geophysical transport, spontaneous stochasticity, mixing, and machine-learning methods for closure and inference. The programme connects theory, simulation and experiment with environmental and climate prediction. It targets doctoral students, postdocs and established researchers in turbulence and transport; exceptional master’s students may be considered. Accommodation is provided for outstation participants.

Develop phylogenetic comparative methods from their probabilistic foundations using Julia. Participants implement likelihood and Bayesian inference, including Markov chain Monte Carlo, and study Brownian motion, continuous-time Markov chains and birth–death processes. These tools are applied to trait evolution, biogeography and diversification, including the effects of environmental and biotic interactions. The practical course is intended for researchers familiar with programming, probability and phylogenetics; prior Julia experience is not required. Sessions on 1, 3, 5, 8, 10 and 12 March 2027, 14:00–18:00 Madrid time (CET). Fee including VAT: EUR 492 through 31 December 2026, then EUR 576; Ambassador Institution rates EUR 393.60 and EUR 460.80. Limited to 18 participants. Acceptance is confirmed before payment. Delivered live online in English with practical exercises. Live attendance is required for a certificate.

The school introduces non-equilibrium statistical physics and stochastic processes as tools for noisy, dynamic systems. Lectures cover transport, carbon nanostructures and carbon nanotubes, theoretical and experimental biophysics, and connections between non-equilibrium physics and problems spanning cosmology to living systems. It is designed for master's and doctoral students and early-career postdoctoral researchers, requiring little prior experience of these advanced topics. Participants submit poster abstracts. There is no registration fee; limited attendance grants prioritize participants from developing countries. The programme supports research training and collaboration across Africa.

Machine learning has enabled advances in protein design and genetic circuits, but biological engineering still faces problems of interpretability, uncertainty and connections between molecular and system-level behaviour. Applied mathematicians, learning researchers and synthetic biologists will explore statistical methods for complex biological data, models that expose mechanisms, stochastic descriptions of noisy systems, multiscale modelling from molecules to populations and robust optimization for design under uncertainty. The workshop aims to develop mathematical foundations for more dependable biotechnology.

The School on Analytical Connectionism is a two-week summer course on analytical tools, including methods from statistical physics and probability theory, for probing neural network models of higher-level cognition, bringing together neuroscience, psychology and machine-learning communities. The 2027 school, held in Boston from 2 to 13 August 2027, focuses on interpretability in reasoning and mathematical cognition: how children or artificial systems use and acquire mathematical concepts, how mathematical intuition and expertise develop and interact, and how studying the heuristics and abstractions that AI systems develop may deepen understanding of human mathematical cognition. The first week offers core lectures on mathematical methods for neural network analysis and on key connectionist models linked to experimental observations; the second week offers frontiers lectures. Participants also join tutorials, poster sessions and contributed talks, work in teams on research projects that they pitch in week one and present at the end, and co-author peer-reviewed lecture notes collected into a special issue. The school is directed by Erin Grant (University of Alberta & Amii) and Stefano Sarao Mannelli (Chalmers University of Technology & University of Gothenburg), with SueYeon Chung (Harvard University & Flatiron Institute) and Andrew Saxe (University College London) as advisors. Applications open on 1 February 2027 and close on 1 April 2027 (AoE); first-round outcomes are communicated on 17 May 2027.