ACAD107492

University of Bristol

ACAD107492

£46974

University of Bristol, Bristol

  • Full time
  • Temporary
  • Onsite working

Posted 2 weeks ago, 25 Apr | Get your application in now before you miss out!

Closing date: Closing date not specified

job Ref: cd9e7ae115c048f4b9a0c2c165439f73

Full Job Description

Bristol Medical School is seeking a talented and enthusiastic postdoctoral scientist with expertise in machine learning. The successful candidate will either be an interdisciplinary scientist developing/applying machine learning approaches to answer health research questions, or be an expert in machine learning and enthusiastic about developing their skills and knowledge in epidemiology / health sciences.

The purpose of this role is to both develop an impactful programme of research in machine learning applied to health and co-lead the Machine Learning and Data Mining for Applied Health Data Science unit of our MSc in Medical Statistics and Health Data Science. The post holder will supervise MSc dissertation projects. They will also support, supervise and advise other researchers in the Bristol Medical School in designing and conducting machine learning research. You will build links and collaborations with research groups/centres in the Department (e.g., the MRC IEU and Bristol BRC), and across the wider University.

What will you be doing?

You will develop a programme of research in a collaborative environment, making use of a wealth of different health datasets, such as phenotypic, 'omics, electronic health records, digital footprint and digital health data. You will contribute to the teaching in the department, by co-leading and teaching on the Machine Learning and Data Mining for Health Data Science unit of the MSc in Medical Statistics and Health Data Science.

Applicants should have a PhD in a quantitative area, preferably in machine learning or including a substantial machine learning component. They should have a strong research track record at a level that is appropriate for their career stage.