Postdoc Fellow – Disease Modelling in NAFLD/NASH

We’re looking for talented scientists to join our innovative academic-style Postdoc. From our centre in Gothenburg, Sweden, you’ll be in a global pharmaceutical environment, contributing to live projects right from the start. You’ll take part in a comprehensive training programme, including a focus on drug discovery and development, given access to our existing Postdoctoral research, and encouraged to pursue your own independent research in cutting edge laboratories. It’s a newly expanding programme spanning a range of therapeutic areas across a wide range of disciplines.
What’s more, you’ll have the support of a leading academic advisor, who’ll provide you with the guidance and knowledge you need to develop your career. This is an exciting area that hasn’t been explored to its full potential, making this an opportunity to make a real difference to the future of medical science.
About the Opportunity:
This position offers high involvement in improving future clinical trials in nonalcoholic steatohepatitis (NASH) patients; being part of a wide network of experts in the fields of NASH, mathematical modelling and clinical development; maintaining a competitive theoretical research output by providing access to a unique selection of clinical longitudinal nonalcoholic fatty liver disease (NAFLD) cohorts and building upon previous in-house developed mathematical models.
This postdoc opportunity involves the application of mathematical methodology, including combined Markov & quantitative systems pharmacology (QSP) modelling and machine learning, to unique clinical longitudinal NAFLD cohorts. The aim is to unveil previously unknown predictive patient-specific factors important for NASH disease progression to inform and optimize clinical development with respect to patient selection, clinical trial duration, as well as human dose prediction.
This is a collaborative project across multiple functions within AstraZeneca R&D, and support will be provided by experts within each function, by renowned academic experts in the field of NAFLD as well as the academic supervisor in this project, Prof. Rohit Loomba (UCSD US), one of the most prominent leaders of NAFLD in the world. This project also offers the opportunity of a secondment with Prof. Loomba at UCSD (University of California in San Diego).
This strong network of experts will help to ensure a high impact publication track record. Thus, the position offers the ability to maintain a competitive theoretical research output. Mentoring of master students and teaching is not required for this position but encouraged and supported.
Our primary objective is to improve the design and evaluation of future clinical NAFLD/NASH trials by developing a framework to predict fibrosis progression using external clinical cohorts, pharmacometric methodology and fit-for-purpose machine learning.
Education and Experience
Essential:
PhD in Applied Mathematics, Statistics, Pharmacometrics or a related field
Programming experience within R/Matlab
Experience with applied statistical modelling

Desirable:
Hands-on experience of working with biological or clinical data
Experience in longitudinal, mixed effects and/or Markov modelling
Scientific excellence as shown by previous work, publication list or references from scientific mentors
Prior understanding of the drug-development process

Required Skills and Capabilities:
Strong computation and programming skills (in R, Matlab or other relevant language)
Proficiency within applied statistical modelling, such as longitudinal, nonlinear mixed effects and/or Markov modelling methods
Excellent written and oral communication skills and ability to work and communicate across disciplines
Curious and passionate about using quantitative methods to advance medicine

This is a 3-year programme. 2 years will be a Fixed Term Contract, with a 1 year extension which will be merit based. The role will be based in Gothenburg, Sweden, with a competitive salary on offer