Postdoctoral fellow

ASTRAZENECA UK LIMITED; Cambridge; On-site; GBP 38,000 - 46,000; Full time

Benefits offered by this job

Academic mentorship

Job summary

AstraZeneca UK Limited's Translational Science and early Clinical Development department in Cambridge is seeking a Postdoctoral Fellow to investigate COPD heterogeneity. You will work at the interface of statistical genetics, machine learning, bioinformatics, and respiratory medicine to model disease progression and response, using large-scale genomic and multi-omics datasets.

You will publish findings, collaborate with the University of Leicester and internal teams, and contribute to precision

Qualifications

• Strong publication record in statistical genetics and genomics.
• Experience analysing GWAS summary statistics and polygenic risk scores.
• Proficiency in programming for data analysis and algorithm development.

Responsibilities

• Conduct ground-breaking research at the interface of statistical genetics, ML, bioinformatics and respiratory medicine.
• Develop approaches using large-scale multi-omics datasets to predict COPD progression.
• Collaborate with AstraZeneca and University of Leicester teams; publish impactful research.
• Contribute to precision medicine strategies for respiratory disease.

Skills

Statistical genetics

Genomics

GWAS statistics

Polygenic risk scores

Deep learning

Machine learning

Programming

Large-scale datasets

Education

PhD in relevant field

Job description

This project is supported by the Translational Science and early Clinical Development department that works in the Respiratory therapy area with the aim of translating cutting edge science into the development of transformative therapies for patients. By harnessing cutting-edge statistical genetics, machine learning, and multi-trait and multi-omics approaches, this project seeks to understand the mechanisms of COPD heterogeneity and develop methodologies for high-resolution polygenic risk scores for COPD. Through the characterisation of pleiotropic genetic influence across respiratory and multimorbid traits, we aim to identify the biological pathways and molecular mechanisms that drive variation in disease progression, treatment response, and patient outcomes. These insights will be leveraged to define clinically meaningful COPD subtypes, improve prediction of disease trajectories, and support the development of precision medicine strategies for respiratory disease. Supported by Glenda Lassi, Associate Principal Scientist at AstraZeneca, and Benjamin Georgi, Principal Scientist at AstraZeneca, you'll also receive academic mentorship and guidance from Jing Chen, Lecturer in Statistical Genetics within the Division of Public Health and Epidemiology, School of Medical Sciences at the University of Leicester. What You’ll Do Our Postdoctoral Fellow will conduct ground-breaking research to understand the biological mechanisms underlying COPD heterogeneity. Working at the interface of statistical genetics, machine learning, bioinformatics, and respiratory medicine, you will develop innovative approaches that leverage large-scale genomic, clinical, and multi-omics datasets to improve our understanding and prediction of COPD progression. You will be part of a collaborative research environment spanning AstraZeneca and the University of Leicester, with opportunities to publish impactful research, engage with multidisciplinary teams, and contribute to the development of precision medicine strategies for respiratory disease. Key Duties & Responsibilities:

• Use advanced bioinformatics, machine learning and statistical genetics methodologies to investigate COPD.
• Lead and drive the project under the supervision of cross-functional AstraZeneca team and in collaboration with the academic expert., You will work in an industry-leading group with a strong publication track record and be supported by a leading academic supervisor, combining the scientific excellence of the University of Leicester with the translational research environment of AstraZeneca. As a Postdoctoral fellow, you will have the freedom to lead innovative research receiving mentorship from experts in statistical genetics, respiratory disease, and data science. You will gain access to world-class computational resources, large-scale multi-omics and healthcare datasets, and extensive opportunities for scientific networking, publication and career development.
  Strong background in statistical genetics and genomics, with experience in analysing GWAS summary statistics and developing polygenic risk scores (PRS).
• Expertise in deep learning and machine learning methodologies, including the development of predictive models.
• Proficiency in programming for data analysis and algorithm engineering, necessary for implementing models and developing user-friendly software packages.
• Experience working with large-scale human genetic datasets for model training, validation, and testing.

Eligibility criteria for the postdoc programme will also be included alongside the essential criteria, as follows: Our Postdoc programme is aimed at individuals with a strong publication record who are either:

• Within 4-years of obtaining their doctoral degree (PhD, DVM, or MD) - PhD already awarded, or
• Students, soon to obtain doctoral degrees (the PhD must be awarded within 6 months of joining AstraZeneca).

Desirable Requirements.

• Familiarity with the genetics of respiratory diseases
• Experience in pharmacogenomics research.

Postdoctoral Fellow - Understanding chronic obstructive pulmonary disease heterogeneity using a multi-trait genetic framework employing high-resolution polygenic risk scores

3 year fixed term contract Cambridge Biomedical Campus, UK Competitive salary and benefits Do you have expertise in, and passion for statistical genetics, bioinformatics, and machine learning? Would you like to apply your knowledge to impact projects in a company that follows the science and turns ideas into life-changing medicines? Then join the Translational Science and early Clinical Development department at AstraZeneca and unlock the power of what science can do!