Post Doctoral Research Associate

Job description

 We are seeking an enthusiastic postdoctoral researcher with a PhD in Statistics, Epidemiology, Data Science, Informatics or related quantitative field, to work on an MRC funded project to understand the links between depression and type 2 diabetes.   

This role is a unique opportunity to apply cutting edge statistical tools to longitudinal data from electronic health records to generate insights into these two major public health challenges. You will have the chance to develop your research skills and academic achievements in a stimulating academic environment. 

In the role, you will use electronic health records from primary and secondary care (from CPRD and UK Biobank) to define and extract clinical fields relevant to depression and diabetes. These rich longitudinal data will include diagnoses, biomarkers and prescribing records. You will then apply statistical modelling methods to characterise the links between these two health conditions.

Projects include:

-        Applying clustering methods to identify type 2 diabetes subgroups, based on blood sugar levels (glycemic control), and assessing the role of depression on these trajectories,  

-        Performing longitudinal modelling to evaluate how timings of diagnosis for depression and for type 2 diabetes impact glycemic control, and,

-        Undertaking time-to-event analyses for depression-related outcomes, based on anti-depressant medication and episode occurrence.

There is scope to use genetic information (from the UK Biobank) in analyses, as well as apply cutting-edge causal inference methods for observational datasets. 

For this role, high-level statistical and computational skills are important - for example you will probably have experience in linux platforms, and statistical programming in R. Previous experience in the analysis of clinical or epidemiological datasets and/or work in electronic health records will be helpful. Full training will be given to enable you to apply your skills to this specific research project. Previous experience of working in mental health and metabolic traits is not necessary.

This research project is a collaboration between King’s College London (Cathryn Lewis) and the University of Exeter (Jess Tyrrell).  The research will focus on longitudinal trends (at King’s) and genetic causal inference (at Exeter), with regular cross-team meetings and the opportunity to visit Exeter.  At King’s, you will join our  dynamic and friendly multi-disciplinary team, which researches the epidemiology and genetics of mental health disorders. We have expertise in statistical modelling using large datasets, and strong clinical links. The SGDP Centre is highly supportive of career development for postdoctoral researchers: you will have the opportunity to develop independence, undertake professional and technical training, with support your next career steps, including applications for fellowship funding.  

This post will be offered on an a fixed-term contract of up to three years, from 1st May 2023 – 31st April 2026 

This is a full-time post, but applications for a part-time role are welcomed. Hybrid working and flexible hours are possible.

Key responsibilities

•        Conduct longitudinal data analyses using electronic health record data to establish the relationships between depression and type 2 diabetes.  

•        Engage in data management and data preparation for research projects. 

•        Lead statistical analyses and write scientific publications arising from the research, as first author.  

•        Work collaboratively with team members at King’s College London and the University of Exeter.  

•        Present results of analyses to team members, and at scientific conferences.  

•        Undertake reproducible analysis in an open science framework, developing analysis pipelines and using github repositories. 

The above list of responsibilities may not be exhaustive, and the post holder will be required to undertake such tasks and responsibilities as may reasonably be expected within the scope and grading of the post.  

Skills, knowledge, and experience

Essential criteria  

1. PhD in statistics, epidemiology, data science, informatics or an allied academic area with strong data analysis component.
2. Experience in the analysis of large clinical or epidemiological datasets.
3. Strong statistical skills, including knowledge of regression methods.
4. Experience with data management and preparing data for analysis.
5. Excellent coding skills and knowledge of statistical programmes (ideally R).
6. Excellent attention to detail (e.g. in data management and reporting results). 
7. Strong interpersonal and written communication skills, with ability to discuss research findings and methods to audiences of different backgrounds. 
8. Experience of writing and publishing papers.
9. Previous involvement in collaborative team science. 

Desirable criteria

1.      Experience with longitudinal data analysis. 

2.      Previous experience analysing electronic health records. 

3.      Active interest in supporting a diverse research environment.  

4.      Commitment to open science through reproducible analysis pipelines and github repository. 

5.      Previous research experience in mental health or metabolic diseases. 

Candidates are strongly encouraged to specifically address the essential criteria outlined in the Person Specification in their covering letter. 

Please note that this is a PhD level role but candidates who have submitted their thesis and are awaiting award of their PhDs will be considered. In these circumstances the appointment will be made at Grade 5, spine point 30 with the title of Research Assistant. Upon confirmation of the award of the PhD, the job title will become Research Associate and the salary will increase to Grade 6.