About The Position
Post Doc Fellow, Data Science for Multi-omics and Biomarker Modelling in Neuroscience Be a part of the legacy: Postdoctoral Research Fellow Program Our Research Laboratories’ Postdoctoral Research Fellow Program aims to be a best-in-industry program for industrial postdoctoral researchers, designed to provide you with an academic focus in a commercial environment. With the resources, reach, and expertise of a large pharmaceutical company, postdoctoral researchers will be positioned to excel in an institution committed to breakthrough innovation in research and discovery. We are a research-driven biopharmaceutical company committed to “following the science” to deliver medicines and vaccines that make a global impact. We focus on critical steps in disease pathways to identify targets and discover compounds that can modify disease. Through innovative thinking, state-of-the-art facilities, and rigorous scientific methodology, we collaborate to advance the next medical breakthrough. We are seeking a highly motivated, data science focused Postdoctoral Research Fellow to join the Neuroscience Translational Analytics team within Data, AI & Genome Sciences Department at our Cambridge site. The successful candidate will build scalable, rigorous computational methods linking biofluid biomarkers with tissue-based multi-omics (e.g. single-cell and spatial omics) to drive precision patient subtyping and predictive modelling in neurodegenerative diseases, with a primary focus on Alzheimer’s disease (AD). You will design end-to-end pipelines from cross-cohort data ingestion and harmonization to modelling, validation, and delivery of tools for in silico target perturbation and biomarker dynamics prediction - centered on AD biology while ensuring methods generalize to other disease and therapeutic areas. You will work closely with data scientists, computational biologists, and neuroscientists to deliver benchmarked models and reusable codebases that enable patient subtype specific mechanism discovery.
Requirements
- PhD within 6 months of hire in Computer Science, Data Science, Biostatistics, Computational Biology/Bioinformatics, or a related quantitative field.
- Demonstrated experience building AI/ML models for multi-omics.
- Strong grounding in probability, statistics, linear algebra, optimization, and numerical methods; deep understanding of modern ML/DL algorithms and classical statistical models, with the ability to read/critique papers and translate state-of-the-art ideas into tailored models.
- Proficiency in Python with solid object-oriented design and software engineering best practices; hands-on experience with deep learning frameworks (e.g., PyTorch) and end-to-end ML workflows; computer science foundations enabling translation from math to production code.
- Strong publication record or other scientific achievements (e.g., awards, patents, grants).
Nice To Haves
- Knowledge of Alzheimer’s disease or neurodegenerative biology.
- Experience with biomarker discovery and/or patient stratification.
- Expertise in one or more of multi-omics integration at scale (genomics, transcriptomics, proteomics; single-cell/spatial preferred), graph/network methods, causal inference.
Responsibilities
- Build and maintain end-to-end AI/ML pipelines for biomarker discovery and patient subgroup stratification using multi-modal data (e.g., multi-omics, clinical/lab values, imaging), including robust ETL (ingestion, QC, normalization, harmonization) and reproducible workflow orchestration.
- Design, implement, and benchmark methods spanning traditional statistics, network and pathway analysis, and modern ML/DL.
- Develop novel models tailored to study objectives: formalize hypotheses, translate mathematical ideas into implementable algorithms, conduct ablation and robustness analyses, and iterate based on emerging data and stakeholder feedback.
- Translate model outputs into actionable insights: produce clear visualizations, summaries; deliver well-documented code, APIs, and reproducible analyses.
- Develop high-quality documentation and internal tools to enable reuse and scaling.
- Collaborate with computational, experimental, and clinical partners on study design, cohort definition, data interpretation, and prioritization of follow-up validation experiments.
- Contribute to scientific communication: publications and preprints, present findings to cross-functional teams, internal and external meetings and conferences.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
Number of Employees
5,001-10,000 employees
