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Job id: 089487. Salary: £43,205 - £51,974 per annum, including London Weighting Allowance.

Posted: 17 May 2024. Closing date: 16 June 2024.

Business unit: IoPPN. Department: Social, Genetic & Dev Psychiatry.

Contact details: Dr David Howard. David.Howard@kcl.ac.uk

Location: Denmark Hill Campus. Category: Research.

About us

Are you interested in conducting groundbreaking mental health research? If so, we are looking for a talented postdoctoral researcher to join our pioneering MRC-funded project. Harnessing the power of machine learning, you will explore the interplay between metabolomic and proteomic data and depression to transform our understanding of this pervasive disorder.

Based at King’s College London, a leading institute for mental health research, you will conduct cutting-edge research supported by our research team in the Social, Genetic & Developmental Psychiatry Centre. You will have the opportunity to advance your skills through courses and training, empowering you to achieve your long-term career aspirations while contributing to the forefront of scientific discovery.

About the role

Depression is a leading cause of disability with 1 in 6 people likely to experience a depressive episode during their lifetime; however, there are no validated biological markers for the disorder. Identifying replicable markers could provide valuable information about the aetiology of the disorder and act as an early warning system prior to the onset of a depressive episode. Depression is a heterogeneous condition warranting the examination of multiple dimensions of depression, including different symptom profiles as well as sex-stratified approaches. This postdoctoral position will enable you to take the lead in examining large-scale biological data to:

1.                  Analyse metabolomic and proteomic data using machine learning to identify those measures related with specific depressive symptom profiles.

2.                  Examine metabolomic and proteomic data to identify sex-specific differences in depression manifestation.

3.                  Apply Mendelian randomization to examine causal relationships between depression and metabolomic and proteomic measures.

There will be fantastic opportunities for you to refine your science communication skills by presenting your research at prestigious international conferences and meetings. There will also be opportunities to engage with diverse audiences, including individuals with lived experiences of depression, through public engagement and public outreach initiatives.

As a valued member of our team within the Statistical Genetics Unit at the Social, Genetic & Developmental Psychiatry Centre, you will be part of a collaborative research environment led by distinguished experts, Dr David Howard (Principal Investigator) and Professor Cathryn Lewis (Head of Department). In this inclusive and supportive environment, you will receive unparalleled mentorship and collaborative opportunities to maximise the impact of your research.

Expert collaborators will also provide additional support to both you and the project as well as providing you with further opportunities for expanding your scientific network.

This is a full-time post (35 hours per week) on a four-year fixed term contract, but a part-time contract will be considered.

About you

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

1.      Ph.D. in statistics, machine learning, or a related academic area with a strong data analysis component (or pending results)

2.      Excellent coding skills in standard languages like Python and/or R with a focus on reproducibility and open-source publishing of code

3.      Experience of analysing large biological datasets

4.      Excellent attention to detail (e.g. in data management and reporting results)

5.      Strong interpersonal and written communication skills

6.      Experience of writing and publishing papers as first author reporting statistical analyses of mental health or other complex traits

For grade 7 post, additionally:

7.      Significant postdoctoral research experience or substantial additional relevant work experience

8.      Strong publication record in the machine learning research field

9.      Ability to take on teaching, supervision, and research management roles

Desirable criteria

1.  Active interest in supporting a diverse research environment

2.  Familiarity with mental health research

3.  Interest in science communication and public engagement activities 

Full details of the role and the skills and experience required, can be found in the attached job description which provided on the next page.

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.

Further information

We pride ourselves on being inclusive and welcoming. We embrace diversity and want everyone to feel that they belong and are connected to others in our community.

We are committed to working with our staff and unions on these and other issues, to continue to support our people and to develop a diverse and inclusive culture at King's. We ask all candidates to submit a copy of their CV, and a supporting statement, detailing how they meet the essential criteria listed in the advert. If we receive a strong field of candidates, we may use the desirable criteria to choose our final shortlist, so please include your evidence against these where possible.

To find out how our managers review your application, please take a look at our ‘How we Recruit’ pages.

This role does meet the requirements of the Home Office and therefore we are able to offer sponsorship for candidates who require the right to work in the UK.

This post is subject to Disclosure and Barring Service and/or Occupational Health clearances.