Dissertation
A. Meddah. Stochastic hybrid dynamical systems for simulating low-grade glioma evolution. PhD Thesis, Johannes Kepler University Linz, 2024. 📃Â
Preprints
J. Mayr, A. Meddah. , I. Tubikanec. Stochastic compliance/evasion dynamics in tax models: a piecewise deterministic Markov process approach. (2026) 📃arXivÂ
 S. Desmettre, A. Mallinger, A. Meddah, I. Tubikanec. Approximate Bayesian computation for stochastic hybrid systems with ergodic behaviour. (2025) 📃arXivÂ
 S. Desmettre, D. Khurana, A. Meddah. The hybrid exact scheme for the simulation of first-passage times of jump-diffusions with time-dependent thresholds. (2025) 📃arXivÂ
Research Papers
 S. Desmettre, D. Khurana, A. Meddah. First-passage time for PDifMPs: an asymptotically Exact simulation approach for time-varying thresholds. JOURNAL OF SCIENTIFIC COMPUTING 108: 94 (2026). 📃 Â
 E. Buckwar,  A. Meddah. Numerical approximations and convergence analysis of piecewise diffusion Markov processes with application to glioma cell migration. Applied Mathematics and Computation 491, 129233 (2025). 📃Â
 E. Buckwar, S. Desmettre, A. Mallinger, A. Meddah. American option pricing using generalised stochastic hybrid systems. Journal of Stochastic Analysis. 6 (1) (2024). 📃Â
 E. Buckwar, M. Conte, A. Meddah  A stochastic hiearachical model for low grade glioma evolution. Journal of Mathematical Biology, 86: 89 (2023). 📃Â
 April 2026 – September 2026: Adaptive Cancer Therapy Strategies via Markov Decision ProcessesÂ
Role: Co-investigator (Lead applicant: Prof. Dmitry Efrosinin, JKU Linz)
Best PhD Thesis Award in Applied Mathematics. Tunisian Association of Women in Mathematics (TWMA) (May 2026)
JKU Young Researchers’ Award 2025. Johannes Kepler University Linz (Jun 2025) 🔗Â