Job Description
Main Activities and Responsibilities
Knowledge Generation
To undertake high quality research and scholarship, including contributing to drafting major grant proposals and/or leading on drafting small grant proposals, and evaluating teaching practice.
To contribute to peer-reviewed publications and other outputs, including as lead author.
To make a contribution to doctoral student supervision, as appropriate to qualifications and experience.
To manage small grants or elements of larger grants, ensuring compliance with good practice in relation to the conduct of research, the ethics policy and other relevant LSHTM policies.
To develop and use Bayesian methods to analyse Electronic Health Records data.
To contribute to data management, processing and synthesis.
To contribute to model design and development.
To conduct simulation studies for testing model validity and generalizability.
Education
To contribute to the delivery of high quality, inclusive, research-informed teaching and assessment in relation to your specific subject and within the broader area covered by your department and disciplinary field.
To contribute to the improvement of the quality of LSHTM’s education, by participating in the development of new and updated learning and teaching materials or approaches.
Internal Contribution
To undertake activities that support the Department, Faculty, MRC Unit or LSHTM.
To reflect LSHTM’s EDI goals in your work and behaviour.
To participate in LSHTM’s PDR process.
External Contribution
To demonstrate good external citizenship by contributing to learned society/conference events, journal and grant reviews.
Professional Development and Training
To keep up to date with the latest research and thinking in your academic field and with changes to pedagogic practice within LSHTM and more generally.
Where the length and nature of the position permits, to apply for and, if accepted, undertake a doctoral degree (if not already acquired).
To undertake and successfully complete the mandatory training required by LSHTM as appropriate to the role.
General
All academic staff are free within the law to question and test received wisdom, and put forward new ideas and controversial or unpopular opinions, to enable LSHTM to engage in research and promote learning to the highest possible standards.
All staff at LSHTM are also expected to:
Act at all times in LSHTM’s best interests.
Treat staff, students and visitors with courtesy and respect at all times.
Comply fully with LSHTM policies, procedures and administrative processes relevant to the role, including when acting as Principal Investigator, accepting academic, managerial, financing and ethical responsibility for a project.
Uphold and support LSHTM’s values (as set out in the LSHTM Strategy).
Act as ambassadors for LSHTM when hosting visitors or attending external events.
Academic Expectations
All academic roles have a statement of academic expectations attached to each level. Please ensure that these have been read and understood. For further information please refer to the Academic Expectations page.
The above list of duties is not exclusive or exhaustive and the role holder will be required to undertake such tasks as may reasonably be expected within the scope and grading of the role. Role descriptions should be regularly reviewed to ensure they are an accurate representation of the role.
Person Specification
Essential Criteria
A postgraduate degree, ideally a doctoral degree, in a relevant topic.
Experience using complex statistical models for real data analysis.
Experience analysing large data sets.
Contributions to written output, preferably peer-reviewed, as expected by the subject area or discipline in terms of types and volume of outputs.
Proven ability to work independently, as well as collaboratively as part of a research team, and proven ability to meet research deadlines.
Evidence of excellent interpersonal skills, including the ability to communicate effectively both orally and in writing.
Evidence of good organizational skills, including effective time management.
Desirable Criteria
Some experience of contributing to research grant applications.
Some experience of teaching and assessment.
Some experience of supervising and supporting junior researchers and/or research degree students, and non-academic staff.
Experience of development of novel statistical methods.
Experience in Bayesian analysis and methods.
Experience in computational implementation of statistical methods.
Experience with analysis of Electronic Health Records data.
Deadline: 10 Oct 2025
