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- Tasks: Teach and research in the exciting field of Statistics at a leading university.
- Company: Join Queen Mary University of London, known for its commitment to equality and diversity.
- Benefits: Enjoy a competitive salary, flexible work arrangements, and support for parental responsibilities.
- Why this job: Be part of a vibrant academic community dedicated to impactful research and advancing women's careers.
- Qualifications: A strong background in Statistics and relevant teaching experience is essential.
- Other info: Applications from women are strongly encouraged to promote diversity in the department.
The predicted salary is between 34800 - 48100 £ per year.
JOB: Lecturer/Senior Lecturer in Statistics, Queen Mary University of London, UK
The School of Mathematical Sciences is seeking to develop further the area of Statistics and is keen to appoint a Lecturer/Senior Lecturer in Statistics.
This is a permanent position, with a salary of £40,865 – £60,109. The deadline is January 11th 2018. To apply, and for more information, see:
The School holds a departmental Bronze Athena SWAN Award and is a registered supporter of the LMS Good Practice scheme. We are committed to the equality of opportunities and to advancing women’s careers. We have policies to support staff returning from long-term absence, for flexible arrangements for staff with parental responsibilities and for child-care support for the attendance of conferences. As part of the School’s commitment to the Athena SWAN and the LMS Good Practice principles we strongly encourage applications from women.
Research Areas
Below we highlight the number of research activities being undertaken and list the names of the members of staff who are active in these areas:
- High-dimensional multivariate statistical modelling (Dr Griffin, Dr Liverani)
- Applied Bayesian statistical inference (Dr Coad, Dr Liverani, Dr Pettit)
- Statistical models for infectious diseases (Dr Griffin)
- Adaptive designs in clinical trials (Dr Coad)
- Design and analysis of computer experiments (Dr Maruri-Aguilar)
- Complex network inference and analysis (Dr Bianconi, Dr Nicosia, Prof Latora)
- Spatial and spatio-temporal modelling (Dr Liverani)
More details on the Statistics group at Queen Mary:
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JOB: Lecturer/Senior Lecturer in Statistics, Queen Mary University of London, UK employer: The International Society for Bayesian Analysis
Contact Detail:
The International Society for Bayesian Analysis Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land JOB: Lecturer/Senior Lecturer in Statistics, Queen Mary University of London, UK
✨Tip Number 1
Familiarize yourself with the specific research areas mentioned in the job description. Highlight your relevant experience and how it aligns with the work being done by current staff members, such as high-dimensional multivariate statistical modeling or applied Bayesian statistical inference.
✨Tip Number 2
Engage with the School of Mathematical Sciences by attending seminars or workshops they host. This will not only enhance your knowledge but also help you network with faculty members, which can be beneficial when applying for the position.
✨Tip Number 3
Demonstrate your commitment to equality and diversity in your interactions and discussions. Since the school values advancing women’s careers and has policies supporting diverse staff, showing your alignment with these values can strengthen your application.
✨Tip Number 4
Prepare to discuss your teaching philosophy and how you plan to contribute to the department's goals. Given the emphasis on teaching and research, having a clear vision of your role in both areas will make you a more attractive candidate.
We think you need these skills to ace JOB: Lecturer/Senior Lecturer in Statistics, Queen Mary University of London, UK
Some tips for your application 🫡
Understand the Role: Make sure to thoroughly read the job description for the Lecturer/Senior Lecturer in Statistics position. Understand the specific research areas mentioned and align your application to highlight your relevant experience and expertise.
Tailor Your CV: Customize your CV to emphasize your teaching experience, research contributions, and any relevant publications in statistics. Highlight your involvement in high-dimensional multivariate statistical modelling or other listed research areas.
Craft a Strong Cover Letter: Write a compelling cover letter that not only outlines your qualifications but also demonstrates your commitment to equality of opportunities and advancing women’s careers, as emphasized by the university.
Highlight Collaborative Experience: If you have experience working in collaborative research environments or have contributed to projects that align with the Athena SWAN principles, be sure to mention this in your application. It shows your ability to work well within a team and contribute to a supportive academic community.
How to prepare for a job interview at The International Society for Bayesian Analysis
✨Know Your Research Areas
Familiarize yourself with the specific research areas highlighted in the job description, such as high-dimensional multivariate statistical modeling and applied Bayesian statistical inference. Be prepared to discuss your own research interests and how they align with the work being done at Queen Mary University.
✨Emphasize Commitment to Equality
Since the School is committed to equality of opportunities and advancing women’s careers, be ready to discuss how you can contribute to this mission. Share any experiences or initiatives you've been involved in that promote diversity and inclusion in academia.
✨Prepare for Teaching Demonstrations
As a Lecturer/Senior Lecturer, teaching will be a significant part of your role. Prepare a short teaching demonstration on a statistical topic, showcasing your ability to engage students and explain complex concepts clearly.
✨Discuss Flexible Working Arrangements
The university supports flexible arrangements for staff with parental responsibilities. Be open about your expectations regarding work-life balance and how you can manage your responsibilities while contributing effectively to the department.
