| Qualification Type: | PhD |
|---|---|
| Location: | London |
| Funding for: | UK Students, International Students |
| Funding amount: | Not Specified |
| Hours: | Full Time |
| Placed On: | 1st April 2026 |
|---|---|
| Closes: | 4th May 2026 |
| Reference: | DB-IOPPN-VISIONB-26 |
We are looking for a talented and motivated candidate to join our new MSCA Doctoral Network: VISIONBRAIN - Cutting-edge Human In Vitro And In Silico Biomedical Tools On Brain Disorders. This EU-funded project will train 15 early-stage researchers to develop human-relevant neuroscience tools beyond animal models, advancing next-generation in vitro and in silico New Approach Methodologies for complex brain disorders.
The successful candidate will join us as a Marie Skłodowska-Curie Actions (MSCA) Doctoral Candidate (fixed-term 3 years, 100%FTE contract of employment) to develop computational models of the neonatal brain. The successful candidate will work on the intersection of neuroscience and new computational methods, combining mathematical and biophysical models with state-of-the-art diffusion and functional MRI data, in typically and atypically developing populations through the lifespan.
About the project:
Using Magnetic resonance imaging (MRI) we are able to assess how brain regions are connected (structural connectivity) and communicate (functional connectivity). In this project we will use mathematical descriptions of neuronal spontaneous activity and network theory, to produce biologically plausible computational models of connectivity in the newborn brain, using state-of-the-art multimodal neonatal and adult MRI data. Computational models allow the inference of whole-brain neuronal dynamic characteristics underlying the association between local functional dynamics and structural connections. Such models help us integrate structural and functional networks, providing information not accessible using single modalities. Importantly, this approach needs to be reconsidered and adapted for the developing brain. To do so, we will use high-quality MRI data in a large cohort of infants (N~1000), including subgroups with clinical conditions. Our approach has the potential to deliver fundamental new insights into developing connectivity in typically and atypically developing individuals, improving our understanding of neurodevelopmental conditions such as autism and attention deficit hyperactivity disorder.
The role involves working collaboratively with a range of stakeholders, including academic and industry partners across Europe. This position is suited for an enthusiastic, curious and self-motivated individual willing to become a leader in the emerging field of computational neuroscience, embracing both computational/mathematical methods and applied neuroscience.
This is a full-time post, and you will be offered a fixed-term contract of employment for 36 months. You will complete a PhD thesis, undertake mandatory secondments (3–6 months) with partner institutions, and participate in network-wide training events.
Note: The applicant must not have resided or carried out their main activity (work/studies) in the United Kingdom for more than 12 months in the 36 months immediately before the recruitment date, and must not already hold a PhD/doctoral degree.
Applicants must complete and submit an online admissions application, via the admissions portal by midnight (23:59 GMT), 4 May 2026.
On the ‘Choosing a programme’ page, please select Department of Forensic and Neurodevelopmental Sciences Research MPhil/PhD Full-time.
More information on the department and the programme is available at the departmental prospectus page here: https://www.kcl.ac.uk/study/postgraduate-research/areas/forensic-and-neurodevelopmental-science-mdres-mphil-phd
Please note there is no need to complete the Research Proposal section in your application as the project has already been set. You are welcome to email Dr Dafnis Batalle (dafnis.batalle@kcl.ac.uk) for more information regarding the project and studentship.
If you have any queries regarding the application process, please contact the Education support team at ioppn.pgr@kcl.ac.uk.
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