01 · Computational Neuroscience
Mechanisms of synaptic learning and plasticity
Stochastic models, dynamical systems and data-driven theory for how synapses store and reshape information.
Mathematics · Neuroimaging · Computational Neuroscience
Maximilian F. Eggl
Computational neuroscience & neuroimaging.
I am a Ramón y Cajal researcher at the Institute of Neuroscience, Alicante (CSIC–UMH). My research uses mathematical modelling and machine learning to study neural systems and develop quantitative methods for diffusion MRI.
NeuroimagingDiffusion MRI · microstructure
Computational neuroscienceSynapses · learning · dynamics
Mathematical modellingInference · control · simulation
Research
Research areas
My work spans quantitative neuroimaging, computational neuroscience and mathematical methods for learning and control.
02 · Imaging Neuroscience
Faster, more informative quantitative MRI
Simulation-based inference and machine learning for robust microstructural biomarkers with minimal acquisition time.
03 · Control & Learning
Optimal decisions in uncertain systems
Adaptive and agnostic control, scientific machine learning and tools for unknown dynamical systems.
Latest workshop
Bio-inspired Deep Learning 2026
The fourth iteration focused on neuromorphic computing, bringing together 16 doctoral students and postdoctoral researchers for hands-on projects at the interface of neuroscience, machine learning and efficient computing.
Neuromorphic computingGuntersblum, Germany2026Led by Prof. Elisa Donati with a project-driven format combining short introductions, close mentorship and intensive group research.
My Team
Building a small, interdisciplinary group.
This section is ready to grow as the team grows.
Maximilian F. Eggl
Ramón y Cajal Researcher
Institute of Neuroscience, CSIC–UMH
Mohammed Zekri
Master's student
Funded by the AstraZeneca InfectoModelling project · Mathematics of pandemics
Recent work