Virtual Brain Twins for the Investigation and Optimization of Temporal Interference Stimulation[...]

Organisation/Company NeuroSchool, Aix-Marseille Université Research Field Psychological sciences » Psychology Biological sciences » Biology Medical sciences Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 26 Apr 2026 - 22:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The NeuroSchool PhD Program of Aix-Marseille University (France) has launched its annual calls for PhD contracts for students with a master's degree in a non-French university.

This project is one of the proposed projects. Not all proposed projects will be funded, check our website for details.

Temporal Interference (TI) is a novel transcranial electrical brain stimulation technique which combines the non-invasiveness of transcranial stimulation techniques with the depth and selectivity of invasive Deep Brain Stimulation (DBS). It has been demonstrated both in animal models and in human studies that TI can selectively stimulate deep targets in the brain without exciting the overlying neural tissues (Grossman et al., 2017; Violante et al., 2023). This renders TI an excellent non-invasive modality for diagnosing and treating various neurological disorders (Missey et al., Brain Stimulation, 2025). From a neuroscientific point of view, TI can be combined with EEG and fMRI to probe the function and connectivity of various brain regions (Missey et al., BioRxiv, 2025). From the applied clinical point of view, TI has potential therapeutic applications in many disorders, like epilepsy, movement disorders, Alzheimer’s disease etc. This project’s objectives are twofold: accurate targeting of TI stimulation, and evaluation of the whole brain response to it. The first objective is essentially an optimization problem of the parameters of the stimulation (i.e. electrode positioning and input signal parameters) with respect to two criteria: (a) accuracy, i.e. steering towards the desired brain region, and (b) focality, i.e. minimizing the stimulation intensity outside the region of interest. The second objective is to investigate the primary and secondary neuronal responses of the brain to the stimulation and to bring them in alignment with previously established therapeutic neurobiological correlates. The first objective will be tackled by developing effective optimization procedures based on a personalized simulation pipeline of the TI intensity distribution that makes use of structural MRI data and electromagnetic simulations. Based on simulation results, global optimization methods along machine learning techniques will be applied for targeting deep brain targets relevant for common neurological disorders. The second objective will be tackled with the help of high resolution whole brain network simulations via The Virtual Brain (TVB) framework. Finally, the results of the the targeting optimization and brain response evaluation will be combined to attain the desired functional outcomes. This PhD project is expected to result in a streamlined in-silico pipeline that can provide optimal personalized TI stimulation parameters for a variety of deep brain targets and respective disorders. Moreover, the active brain response to the stimulation will be evaluated to guide fundamental neuroscientific research and clinical applications. Feasibility of the project is ensured through the lab’s long experience, expertise and excellence in virtual brain twins, an extensive network of collaborations, and tight exchange with the ongoing NAUTILUS research project on TI, funded by “Impact Santé”, which can provide in-silico infrastructure and experimental results for validation (sEEG and fMRI recordings).

Expected candidate profile : The prospective candidate is expected to come either from the neuroscientific/medical field with a strong background in computational science or from the engineering/physics field with a specialization or strong interest in biomedical applications and neuroscience. Necessary skills include scientific computing and programming, ability to cross interdisciplinary boundaries, strong teamwork abilities, very good English knowledge, and scientific communication.