Current Vacancies
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Key details
- Location UK DRI at Imperial
- Salary: £50,733 - £59,484 per annum
About the role
Following recent awards from both the Michael Uren Foundation and The Wellcome Trust, Dr Samuel Barnes invites applications for postdoctoral Research Associates to take leading roles in high-impact, in vivo systems neuroscience research. The Barnes Lab, part of the UK Dementia Research Institute investigates vulnerability in the ageing brain, aiming to understand the role of neural circuit plasticity in the healthy adult brain, ageing and early-stages of neurodegeneration.
Project 1: Investigation of tau and alpha-synuclein pathology on synaptic vulnerability and in vivo circuit dysfunction
You will investigate how the interaction between alpha synuclein and tau pathology can drive synaptic and circuit dysfunction in vivo. This research leverages a novel Light Field Imaging platform capable of volumetric voltage imaging at kilohertz rates, expansion microscopy and synaptic spatial proteomics in combination with recently developed mouse models of pathology to understand how tau and alpha-synuclein pathologies, and the combination of the two, can trigger synaptic dysfunction.
Project 2: Real-time volumetric voltage imaging and bioelectronic targeting of Alzheimer's Disease related circuit dysfunction
You will develop non-invasive, closed-loop interventions for Alzheimer’s disease (AD) circuit dysfunction. This research leverages a novel Light Field Imaging platform capable of volumetric voltage imaging at kilohertz rates. The project aims to integrate this real-time imaging with Temporal Interference (TI) brain stimulation to investigate and rescue neurovascular and circuit-level deficits in AD-related mouse models. See news article below:
What you would be doing
For both projects, we the candidates will execute the delivery of complex in vivo imaging and neuromodulation experiments, together with:
- Multidisciplinary Collaboration: Interface directly with co-investigators interested in pathology and imaging technologies, bioengineering, computing, and signal processing.
- Mentorship & Output: Supervise PhD/ Masters students and lead the preparation of high-impact manuscripts.
- Funding: You will be actively supported in pursuing independent fellowship applications and collaborative grant opportunities.
What we are looking for
You will hold (or be near completion of) a PhD in Systems Neuroscience, Neuroengineering, or a related quantitative biological field, and demonstrate:
- Technical Expertise: Significant experience with in vivo optical imaging, electrophysiology, synaptic proteomics and rodent surgical techniques.
- Analytical Skills: Proficiency in coding and handling large-scale neural datasets is highly desirable.
- Professionalism: A track record of peer-reviewed publications and the ability to work within a highly collaborative, multi-site team.
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Key details
- Location UK DRI at Imperial
- Salary: £50,733 - £59,484 per annum
About the role
Launched in 2017, the UK DRI stands as a beacon of scientific innovation, representing an unprecedented £300 million investment in dementia research - the largest of its kind in UK history. The purpose of the UK DRI is to transform the outlook for people living with or at risk of neurodegenerative conditions through research.
Applications are invited for a postdoctoral Research Associate in brain vascular epigenomics, funded by the Michael Uren Foundation, to join the laboratory of Dr Alexi Nott (https://nottgroup.com/index.html) in the UK DRI at Imperial College London.
Vascular dysfunction is one of the earliest pathological features of dementia, preceding clinical onset and representing a critical but underexplored window for therapeutic intervention. Our recent work has demonstrated that the brain neurovascular unit is a key driver of genetic risk for small vessel disease, and that epigenomic profiling of vascular cell types can be used to prioritise repurposable drug targets for dementia (Ziegler et al., Neuron, 2026). Building on these findings, the post holder will lead a translational research project investigating the cell-type-specific gene regulatory mechanisms underlying small vessel disease, with the aim of identifying and validating targets of vascular dysfunction relevant to dementia. This work carries direct translational potential, with the opportunity to contribute to the prioritisation of both repurposable and novel therapeutic candidates underpinned by human epigenetic and genetic evidence.
What you would be doing
Using your experience in (epi)genomics of the brain, you will:
- Generate and analyse large-scale epigenomic and multi-omics datasets from human vascular and immune cell types isolated from post-mortem brain tissue, to identify signalling pathways and transcription factors dysregulated in small vessel disease and dementia.
- Contribute to the functional interpretation of noncoding disease risk variants linked to vascular dysfunction, guiding the identification of genetically supported therapeutic targets.
- Work collaboratively as part of a multidisciplinary research team, including computational scientists and experimentalists, within the UK DRI and with external collaborators.
What we are looking for
We are seeking a motivated and organised researchers who is excited by the science we do! You will have:
- Demonstrable hands-on experience in (epi)genomics techniques such as CUT&Tag, ChIP-seq, or ATAC-seq, ideally in the brain.
- A strong foundational understanding of neuroscience, or a closely related discipline, with knowledge of gene regulatory mechanisms and an appreciation of how epigenomics can be used to interpret genetic variation.
- Experience with nuclei isolation and/or fluorescence-activated nuclei sorting (FANS) or FACS, preferably from brain or human tissue.
- Experience with R or Python and familiarity with bioinformatics pipelines would be advantageous but is not essential.
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Key details
- Location: UK DRI at Imperial
- Salary: This PhD position includes a tax-free stipend of £23,805 per annum and Home fees for 3 years, and up to six months of writing up stipend if required.
About the Project
Applications are invited for a 3-year PhD studentship funded by the Edmond J. Safra Foundation within Dr Cynthia Sandor’s group in the Department of Brain Sciences at Imperial College London.
This studentship will focus on:
Predicting Insulin Resistance from Blood Transcriptomes: A Peripheral Window onto Brain Insulin Resistance in Parkinson’s and Alzheimer’s Disease
The successful candidate will be based at Imperial’s White City Campus and will join an active and collaborative postgraduate community. The project will be supervised by Dr Cynthia Sandor, an Edmond and Lily Safra Assistant Professor in Parkinson's Disease in the Department of Brain Sciences, and a UK DRI Group Leader.
Background
Parkinson’s disease affects over 10 million people worldwide and its prevalence is increasing with ageing populations. Type 2 diabetes and insulin resistance are established risk factors for Parkinson’s disease. Increasingly, attention has turned to brain insulin resistance, a state in which neurons themselves respond poorly to insulin, with impaired signalling. Unlike peripheral insulin resistance, brain insulin resistance reflects blunted neuronal insulin action and has been linked to mitochondrial dysfunction, α-synuclein aggregation, and neuroinflammation, yet its genetic and biomarker basis in Parkinson’s disease remains poorly understood.
Recent work from Dr Sandor’s group and collaborators (Volpato et al. 2025) has shown that vulnerable dopamine-producing neurons show increased activity in renin–angiotensin system signalling and metabolic stress pathways, and that genetic risk for Parkinson’s overlaps with risk for type 2 diabetes and cardiometabolic traits. Epidemiological studies also suggest that certain blood pressure and diabetes medications may reduce Parkinson’s risk, but we do not yet know which patient subgroups benefit most. A central obstacle is that brain insulin resistance cannot be measured directly at scale in patients, which motivates an accessible, blood-based readout that can stratify individuals and probe the metabolic dimension of neurodegeneration across large cohorts.
Project Overview
This PhD project will develop and validate a computational model that predicts insulin resistance from peripheral blood mononuclear cell (PBMC) transcriptomes, and apply it to existing Parkinson’s and Alzheimer’s disease PBMC datasets. Peripheral insulin resistance is directly predictable from blood and is only partially coupled to brain insulin resistance, so the PBMC-derived score is positioned as a scalable, blood-based proxy for the central process rather than a direct measure of it. The project is entirely computational and combines machine learning with single-cell and bulk transcriptomics and rigorous cross-dataset validation.
Key objectives include:
- Signature derivation: Train a transferable insulin-resistance predictor on metabolically labelled reference cohorts using regression and classification models with per-cell-type pseudobulk analysis to localise the signature to specific immune subsets.
- Data integration & validation: Harmonise heterogeneous single-cell and bulk PBMC datasets (using Harmony, scVI/scANVI and ComBat), explicitly controlling for age, sex, BMI, medication and ancestry, and validate the signature with strict leave-one-dataset-out cross-validation and an external labelled cohort.
- Transfer to PD cohorts: Apply the validated signature to existing Parkinson’s PBMC datasets to impute per-subject and cell-type-resolved insulin-resistance scores, and test whether these scores are elevated relative to controls and track disease severity and cognition.
- Brain-IR alignment: Where central insulin-resistance anchors are available (for example intranasal-insulin EEG/fMRI, FDG-PET, or published brain insulin-resistance signatures), correlate the peripheral score with these measures to establish how far the blood-based readout reflects the brain process.
Training and Environment
The student will receive training in:
- Machine learning for transcriptomic prediction (regression and classification)
- Single-cell and bulk RNA-seq analysis, batch integration and cell-type deconvolution
- Rigorous cross-dataset validation, confound control and reproducible research
- Integrative analysis of multi-omic and clinical data across neurodegenerative cohorts
This project provides an excellent opportunity to gain experience at the interface of machine learning, single-cell transcriptomics, and translational neuroscience.
Requirements
Applicants should hold a First Class or Upper Second-Class degree (or equivalent) in bioinformatics, computational biology, data science, statistics, machine learning, or a related quantitative discipline. A Master’s degree in a relevant field (data science, computational neuroscience, statistical genetics) is desirable. Strong programming in R or Python and an interest in single-cell transcriptomics, machine learning and large-scale cohort analyses are advantageous.
Application Process
To apply, please send the following to Dr Cynthia Sandor (c.sandor@imperial.ac.uk) or Dr Katarzyna Zoltowska (k.zoltowska@imperial.ac.uk).
- Your CV
- A brief statement outlining your research interests and motivation
- Contact details for two academic referees
For informal enquires, please contact Dr Sandor or Dr Zoltowska.