Current Vacancies
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Key details
- Location UK DRI at UCL
- Salary: £43,981-£52,586 per annum
About us
The UK Dementia Research Institute (UK DRI) is the biggest UK initiative supporting research to fill the major knowledge gap in our basic understanding of the diseases that cause dementia.
Research from UK DRI at UCL covers the journey from the patient to the laboratory and back to the patient with improved diagnosis, biomarkers and candidate therapies put to the test.
About the role
We are seeking a Centre Research Administrator to support the Centre Manager with the provision of a high-quality operational and administrative support function to scientists and their laboratory research teams within the UK Dementia Research Institute (UK DRI) at UCL, helping to ensure the smooth running of the Centre.
As the first point of contact for research, technical and academic staff, you will support a wide range of activities, including research grant costings, financial administration, event coordination, committee support, onboarding of visitors and honorary staff, and internal communications. You will work closely with colleagues across the UK DRI at UCL, the UK DRI Central Team, the Queen Square Institute of Neurology, and UCL's central services.
The role is available immediately and funded by UK Dementia Research Institute Ltd until 31 March 2028 in the first instance.
This role is eligible for hybrid working with a minimum of 60% of time on site.
About you
You will have experience of research grant administration, including research funding applications, financial costings, post-award budget management and associated financial processes. Experience of working in an administrative role within Higher Education is essential, as is experience of independently organising events or managing small projects. High-level numeracy skills, confidence in managing and analysing financial data using tools such as Excel, excellent interpersonal and communication skills, strong problem-solving abilities, and good organisational skills are also a requirement for this role.
This role does not meet the eligibility requirements for a Skilled Worker Visa certificate of sponsorship under UK Visas and Immigration legislation. Therefore UCL will not be able to sponsor individuals who require right to work in the UK to carry out this role.
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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.