Postdoctoral Researcher

Role Purpose

The postdoctoral researcher will lead the proteomics component of an ambitious international research programme funded by Aligning Science Across Parkinson's (ASAP). Working at the interface of cell biology, neuroscience and quantitative proteomics, you will use state-of-the-art mass spectrometry to uncover how the major genetic causes of Parkinson's disease remodel the lysosomal system, identify new biomarkers and reveal opportunities for therapeutic intervention.

Service Delivery

• Deliver high-quality experimental research in signal transduction and Parkinson’s disease biology, generating reproducible and well-documented datasets.
• Apply advanced methodologies (e.g. organelle isolation, proteomics, PTM analysis) to support core research objectives.
• Contribute to the development and optimisation of new experimental workflows, including high-throughput and single-cell approaches.
• Ensure timely delivery of project milestones, maintaining rigorous standards of data quality, reproducibility, and open science.
• Support the Unit’s translational research aims by contributing data relevant to biomarker discovery and therapeutic development.

Communication

• Communicate research findings clearly through written reports, publications, and presentations at internal meetings and international conferences (independent).
• Engage in regular discussions with supervisors and collaborators to interpret data and refine experimental strategies (independent and collaborative).
• Contribute to grant writing and preparation of manuscripts (independent with support).
• Provide guidance and technical input to students and junior researchers where appropriate (support role).
• Communicate effectively with multidisciplinary teams, including computational, clinical, and industry collaborators.

Liaison & Networking

• Liaise regularly with researchers within the MRC PPU and Faculty of Life Sciences, including proteomics platforms and core facilities.
• Interact with collaborators across the Division of Signal Transduction Therapy (DSTT), including pharmaceutical partners.
• Engage with external academic collaborators working on Parkinson’s disease, lysosomal biology, and proteomics including ASAP-CRN network
• Participate in national and international scientific networks, contributing to collaborative projects and consortia.
• Build professional networks to support dissemination, collaboration, and career development.

Decision Making, Planning and Problem Solving

• Work independently to design and execute experiments, possess strong analytical skills to troubleshoot technical challenges, and interpret complex datasets.
• Make informed decisions on experimental approaches, prioritisation of research objectives, and data analysis strategies.
• Plan and manage own workload to meet project timelines, adjusting priorities as required. • Contribute to the planning of collaborative projects and shared resources.
• Apply critical thinking to solve methodological and biological problems, including optimisation of experimental systems and workflows.

Team Development

• Work as part of a collaborative, multidisciplinary research team within the MRC PPU.
• Provide informal supervision, mentoring, and technical support to PhD students and junior researchers.
• Contribute to a positive, inclusive, and supportive team environment.
• Share expertise and promote best practices in experimental design, data analysis, and reproducibility.
• Participate in training activities and contribute to the development of team capabilities where appropriate.

Knowledge and Experience

• A PhD (or near completion) in a relevant discipline
• A Strong academic track record, including at least one first-author publication (or preprint)
• Expertise in two or more of:
  • Quantitative mass spectrometry and computational proteomics
  • Biochemistry and interest in signal transduction and/or translational research.
  • Hands-on experience with Orbitrap and/or timsTOF instrumentation.
  • Experience in analysing proteomics datasets using Python or R.