Current Vacancies
-
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.
-
Key details
- Location: London/Hybrid
- Salary: £76,000 - 84,000
- Deadline: 15 November 2026
Based at our London office, we're looking for a Research Engineer - Lab Brains & Scientific Workflows. Help build a research institute that remembers what it has learned and puts that knowledge to work. You can learn more about our ambitions here.
You will build Lab Brains: agents and connected tools that help laboratories understand their work, recover its history and follow through on what needs to happen next. You will work directly with researchers, technicians and lab managers, turning their recurring problems into software they choose to use.
Imagine asking why a protocol changed, receiving the relevant experimental record and meeting decision, and preparing the right follow-up in the lab's project system. Or helping a new researcher understand what has already been tried, which result remains uncertain and who can help. You will make workflows like these dependable, inspectable and easy to correct.
This is a hands-on technical leadership role with room to shape the product and influence the Institute’s use of AI. We want someone who thinks big and enjoys programming, trying unfamiliar tools, finding a small useful starting point and improving it through real use. You will be a founding member of the Institute-wide AI initiative, working as a peer with an engineer focused on scientific knowledge and field building.
What you'll build• Useful lab workflows connecting meeting notes, approved Slack conversations, Linear projects and Labstep experimental records, with links to the relevant code, data and documents. Start with a small agreed pilot and extend what works.
• Agents and tools that recover experimental history, preserve decisions and negative or inconclusive results, identify missing context and propose follow-up that users can inspect and approve.
• Reusable integrations that handle changing records, permissions, retries and failures, and keep the authoritative systems connected without creating extra manual record-keeping.
• Interfaces and review controls that help people see where an answer came from, correct it and choose what may be shared beyond their lab.
• A tested route for distributing useful tools across participating labs, with documentation, dependable releases and clear ownership for support.
You will compare existing agents, scientific software and commercial tools on our actual workflows and develop a strategy for adoption across the Institute where they fit and leading development where a clear need remains. You will champion use of these agents to senior management, explaining the benefits they bring to the Institute and proactively revisit them as the ecosystem changes.
How you will work
Spend time with the people doing the science, then ship and improve small working products. Evaluate task completion, source accuracy, net time saved, recurring use, running costs and failure modes. Keep the mistakes and rejected suggestions visible so the team learns from them.
Treat reproducibility as part of the product: preserve the versions, methods, code and evidence needed to inspect a result or repeat an analysis. Build permissions, human approval and correction into the workflow. Researchers should gain useful memory and control over their work.
With the Scientific Knowledge & Field Building engineer, connect selected, authorised lab knowledge to wider scientific questions and return useful requests or opportunities to the lab. Both engineers share responsibility for an end-to-end workflow that improves real work.
What you bring
• Strong programming ability and evidence of shipping software that other people use, with the ability to learn the languages and tools needed by the codebase.
• Sound software engineering practice: Git with GitHub or equivalent, code review, automated testing, continuous integration, documentation, packaging, dependency management and maintainable releases.
• Practical experience building LLM applications or comparable intelligent automation, including tool/API integration, evaluation and debugging.
• Experience connecting systems and handling authentication, data access, changing records and operational failures.
• Product judgement: you can discover a useful problem with users, choose a manageable first version and improve it from feedback.
• A commitment to reproducible science and collaborative software development, demonstrated through examples of work others can inspect, run or improve.
• Enthusiasm and ability to influence how an organisation can drive change through technology adoption.
• The ability to work autonomously and lead: set priorities, make and justify technical and strategic decisions, bring others with you and take ownership of outcomes.
Experience with research software, laboratory workflows, scientific computing, developer tools, open-source packages, electronic lab notebooks or task systems would be valuable. You do not need prior experience with every named platform. We welcome evidence from industry, startups, academia, open source and independent projects; no particular degree or PhD is required where equivalent capability can be demonstrated.
-
Key details
- Location: London/Hybrid
- Salary: £76,000 - 84,000
- Deadline: 15 November 2026
Based at our office in London, we're looking for a Research Engineer - Scientific Knowledge and Field Building. Help UK DRI understand what is known, where the evidence disagrees and what work could make the biggest difference next. You can learn more about our ambitions here.
You will develop the strategy and build software that makes these questions easier to investigate and act on. Starting with a focused Alzheimer’s disease research question, you will help scientists create and maintain an inspectable map of claims, evidence, disagreements, capabilities and possible routes towards a meaningful outcome. Over time, that work should connect published literature with selected, authorised unpublished research and the knowledge held across UK DRI laboratories.
We are looking for a technical leader who thinks big and cares about how science establishes knowledge and how institutions help people learn and work together. You might already be interested in discourse graphs, claims and evidence, outcome graphs, reproducibility, open science or new ways of organising research. You might have encountered the same problems while building search tools, scientific data systems or collaborative software. We value the ability to turn that curiosity and ability to think big into useful working systems.
You will be a founding member of the UK DRI AI initiative, working as a peer with an engineer building Lab Brains and scientific workflows. Together, you will connect what laboratories learn and influence what the institute can understand and do.
What you'll build• A practical representation of scientific questions, scoped claims, supporting and opposing evidence, sources, methods and uncertainty. Keep exact source links, versions and the distinction between proposed and reviewed interpretations.
• Ingestion, extraction, reconciliation and update pipelines for an agreed scientific corpus, with interfaces that make it easy for researchers to inspect, qualify and correct the results.
• Views that reveal where findings agree, genuinely disagree or cannot yet be compared, without treating missing evidence as evidence against a claim.
• A first Alzheimer outcome map: work with scientists to describe what must become possible for a selected outcome, the alternative routes, unresolved assumptions, missing capabilities and next decisions.
• Connections between that map and reviewed institutional capabilities, datasets and developing research, making potential collaborations or shared bottlenecks specific enough for people to evaluate.
• Decision dossiers and feedback tools that record what was considered, what an accountable owner chose, what was tried and how subsequent evidence changed the picture.
Choose data models, search and graph technologies according to the task. Learn from communities such as Discourse Graphs and existing outcome-planning approaches, compare available tools and publish or contribute reusable work where permissions and licensing allow.
How you will work
Build with scientists and methodologists, combining software development with focused scientific review. Help reviewers make disagreement explicit and productive. Design the workflow so that maintaining the shared account requires less effort than reconstructing it repeatedly.
Lead on system evaluation against ordinary search and structured evidence review on the same questions and sources. Measure evidence quality, scope errors, review effort, correction and whether the work improves a real decision or hand-off. Preserve failed ideas, alternative interpretations and justified no-action decisions.
Reproducibility is central: a reader should be able to inspect the source and analysis behind a claim, understand its limitations and see what changed. Laboratory owners retain control over what is shared. Scientific and programme decisions remain with accountable people, supported by the software you build.
Influence how senior management embraces AI across the Institute to deliver its mission faster.
What you bring
• Strong programming ability and evidence of delivering useful software, with the ability to learn the languages and tools needed by the codebase.
• Sound engineering practice: Git with GitHub or equivalent, code review, tests, continuous integration, documentation, packaging, dependency management and maintainable releases.
• Experience modelling and integrating complex information, for example in scientific data systems, search, evidence synthesis, collaborative knowledge tools or research software.
• The ability to reason carefully about evidence, ambiguity, provenance and changing interpretations, and translate that reasoning into usable software.
• Practical experience applying and evaluating language models or related information-extraction/retrieval methods, with a clear account of their failure modes.
• Curiosity about how scientific communities establish knowledge and coordinate work, and a commitment to reproducibility, open collaboration and constructive disagreement.
• Enthusiasm and ability to influence how an organisation can drive change through technology adoption.
• The ability to work autonomously and lead: set priorities, make and justify technical and strategic decisions, bring others with you and take ownership of outcomes.
Experience with knowledge graphs, ontologies, discourse graphs, evidence synthesis, interface design, biomedical research or open-source scientific software would be valuable. Prior expertise in a particular graph database, a particular programming language or Alzheimer's research is not required. Scientific judgement will be developed with domain experts. We welcome varied professional and educational paths; no particular degree or PhD is required where equivalent capability can be demonstrated.
-
Key details
- Location UK DRI at Edinburgh
This 4-year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership (DTP).
In the rapidly evolving field of medical imaging, the integration and analysis of data from diverse acquisition systems present a critical challenge. Variability in imaging parameters and modalities hampers the consistency and reliability of analyses when combining data from different clinical settings and analysing longitudinal trajectories. Current harmonisation techniques are suboptimal in clinical settings. Phenotypes from images acquired with the most common protocol and under the scanners at major hospitals can bias automated pipelines, unfairly impacting patients imaged with disease-specific acquisition protocols and/or on scanners at health centres.
Another source of bias is caused by the population sample. Different levels of prevalence of diseases that reflect in structural brain changes also bias computational brain analyses. Automated phenotype extraction methods are often developed and validated in relatively homogeneous research datasets, whereas routine clinical imaging encompasses much greater variation in anatomy and pathology. The extent to which this heterogeneity affects measurement reliability, reference trajectories and comparability across datasets remains poorly characterised.
This project has the following aims:
1. Develop and validate robust extraction pipelines for technically challenging imaging phenotypes from heterogeneous routine clinical images: fractal cortical complexity, brain health index, white matter damage, brain volumes, white matter hyperintensities, and infarcts.
2. Quantify the influence of scanner, sequence, imaging modality, and acquisition parameters on each phenotype and develop harmonisation/QC strategies that preserve biological age- and sex-related variation.
3. Derive age- and sex-stratified clinical reference distributions for these phenotypes, with particular emphasis on measures for which large-scale reference values do not currently exist, and compare them with those acquired from research populations.
4. Test the reproducibility and clinical informativeness of these reference trajectories across independent datasets and diagnostic groups.
The student will receive training in medical image processing, big data analyses, working in the safe heaven, and advanced statistics. The principal supervisor will give day-to-day supervision in brain image processing and analysis methods, and the harmonisation of brain imaging data.
Funding Notes
This opportunity is open to UK and international students and provides funding covering stipend and UK level tuition fees.
-
Key details
- Location UK DRI at Imperial
- Salary: PhD position includes a tax-free stipend (for guidance, the UKRI London rate 2026/27 is £23,805) and UK/Home fees for 3.5 years.
The project
Cerebral small vessel disease (SVD) is a major cause of stroke and vascular dementia, and a key contributor to Alzheimer's disease. SVD is highly heritable, but most genetic risk variants lie in non-coding DNA, and how they disrupt gene regulation is largely unknown. This project will build the first integrative map of gene regulatory mechanisms in SVD, linking human genetics to molecular function. The aim is to find new blood biomarkers and genetically supported drug targets.
You will lead the analysis of a unique, deeply phenotyped cohort: 147 longitudinally followed SVD patients from the Mild Stroke Study 2 (MSS2). The cohort has genotyping, plasma proteomics, brain imaging, blood–brain barrier measures and 9 years of clinical follow-up. New DNA methylation (EPIC array) and RNA-seq data will be generated for the project. You will compare these with matched controls from the AIRWAVE cohort (~700 samples) and the Lothian Birth Cohort 1936.
- Aim 1 (years 1–2): Find DNA methylation and gene expression changes linked to SVD subtypes, dementia progression, polygenic risk, cardiovascular risk factors and epigenetic ageing.
- Aim 2 (years 3–4): Prioritise causal genes and variants. Methods include meQTL/eQTL analysis, multi-trait analysis with cardiovascular traits and other dementias, colocalisation and Mendelian randomisation. You will integrate the results with histone modification (CUT&Tag), single-cell multiome and proteomic data.
This is a computational (dry-lab) PhD. You will receive training in:
- statistical and population genetics
- epigenomics and multi-omic integration
- causal inference (Mendelian randomisation)
- reproducible analysis on high-performance computing
Training is provided by an interdisciplinary team spanning functional genomics, genetic epidemiology and clinical neuroimaging.
Supervisory team
- Dr Alexi Nott, Imperial College London (functional epigenomics and gene prioritisation)
- Prof Joanna Wardlaw, University of Edinburgh (clinical SVD and stroke, MSS2 cohort)
- Dr Axel Montagne, University of Edinburgh (vascular biomarkers, blood–brain barrier)
- Prof Abbas Dehghan, Imperial College London (genetic epidemiology, QTL analysis)
- Dr Fotios Koskeridis, Imperial College London (multi-trait genetics, cardiovascular traits)
Who we're looking for
- A first or upper-second class degree (or equivalent) in genetics, genomics, bioinformatics, computational biology, statistics, epidemiology, data science, neuroscience or a related quantitative discipline. A relevant Master's degree is desirable but not essential.
- Essential: programming experience in R and/or Python, and a strong interest in using data to understand human disease.
- Desirable: experience with Unix/HPC environments, statistical genetics (GWAS, QTL, Mendelian randomisation), omics data analysis (methylation arrays, RNA-seq, epigenomics) or large cohort datasets.
- You do not need wet-lab experience.
Funding
The studentship covers 4 years of:
- home tuition fees
- a tax free stipend, at least equal to the UKRI London rates (for 2026/27 this is £23,805)
- £5,000 per year for research costs
- £1,000 per year for conference travel
Additional funds can also be applied for to support training and career development.
Eligibility: home fee status only. The studentship covers home-rate tuition fees only, so we can only consider applicants who qualify for home fee status.