Help Shape the Future of Stroke Rehabilitation and Brain Stimulation 

We invite applications to join the Brain Dynamics Lab—an interdisciplinary research team at the  interface of dynamical systems theory, brain network modelling, and neurostimulation-based  rehabilitation. Our mission is to uncover and exploit the mechanisms of brain plasticity following  stroke and neuromodulation, by combining cutting-edge modelling with real-world  neurotechnology. We regard recovery not as anatomical repair, but as a problem of  restoring network-level brain dynamics through non-invasive electrical stimulation (tDCS/tACS).  We model the brain as a dynamical system perturbed by lesion, reconfigurable via stimulation dependent plasticity. 

Paid positions according to the academic level 

Location: 

Valencia, Spain 

Chile [Valparaiso, Santiago, Talca, Concepción, flexible within collaborating institutions] 

Positions Available: 

• PhD studentships 

• Postdoctoral research fellows 

• Research assistants (MSc-level support roles) 

Fields: Physics, Biomedical Engineering, Electronics/Telecom/Control/Informatics Engineering,  Applied Mathematics, Computer Science, Computational Neuroscience 

What You’ll Work On 

Successful applicants will join a growing team working on projects including: • Whole-brain network modelling (Kuramoto, Wilson-Cowan, neural mass models) to simulate  post-stroke disruption and plastic recovery 

• Computational modelling of plasticity mechanisms (Hebbian, homeostatic, STDP, resonance tuned) across varying brain states 

• Control theory in brain networks, including stimulation as control input to re-establish  metastability and synchrony 

• Analysis of electrophysiological and imaging data (EEG, TMS, lesion mapping) from healthy  and clinical populations 

• Development of simulation-guided protocols for individualized, state-dependent stimulation

These positions will support Masters or PhD theses, contribute to postdoctoral innovation, and  underpin a translational pipeline from virtual lesion modelling in healthy participants to patient specific neurorehabilitation strategies. 

We are looking for: 

We welcome candidates from a broad range of backgrounds, including: 

• Physicists with expertise in nonlinear dynamics or network theory

• Electrical or Telecom Engineers with skills in signal processing, control theory, modelling, or  brain–computer interfaces 

• Biomedical Engineers experienced in neuroimaging, neurorehabilitation, or neural interfaces • Applied Mathematicians or Computer Scientists with interests in simulation, control or  computational neuroscience 

We value curiosity, collaboration, and a commitment to translational science. Programming skills in Python, Matlab or C++ are required.  

Prior experience with neural modelling (e.g., Kuramoto, neural masses), data analysis  (EEG/fMRI), high performance computing (HPC), or brain stimulation (tDCS/tACS/TMS) is a  strong plus but not required. 

Why join us now 

This is an exciting moment in the field. Advances in connectome-informed modelling, network  control theory, and neuromodulation are converging to make precision neurostimulation a  clinical reality. As part of our team, you’ll be at the frontier of: 

• Designing next-generation rehabilitation tools 

• Validating models with real-world clinical and experimental data 

• Publishing in top-tier journals (e.g., Network Neuroscience, Neuroimage, PLOS Comp Biol) • Collaborating with international partners in Spain, Portugal, Chile, and the UK 

How to apply 

Send academic CV and one page letter of motivation describing your research interests, and fit  with the Brain Dynamics Lab to: 

Chile Alejandro.Weinstein@USM.cl 

Spain Wael.Deredy@ValgrAI.EU 

Selected group publications 

https://arxiv.org/abs/2511.14065

https://www.biorxiv.org/content/10.1101/2025.04.30.651283v2.abstract https://www.nature.com/articles/s41598-025-27606-5 

https://www.nature.com/articles/s41598-025-14299-z

https://ieeexplore.ieee.org/abstract/document/10937758

https://doi.org/10.1098/rstb.2023.0093

https://link.springer.com/article/10.1007/s11571-024-10093-1