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- Tasks: Lead innovative research in statistics to tackle global health challenges.
- Company: Join a prestigious team at Imperial College London and collaborate with top researchers.
- Benefits: Competitive salary, hands-on training, and mentorship from leading scientists.
- Why this job: Make a real impact on global health while developing cutting-edge statistical tools.
- Qualifications: Experience in statistics and a passion for global health and conservation.
- Other info: Access to unique datasets and excellent career development opportunities.
The predicted salary is between 43093 - 50834 £ per year.
Research Associate in Modern Statistics, Global Health, and Conservation Ecology
Jul 24, 2023
Salary Range: £43,093- £50,834 per annum
Fixed Term for initially 12 Months with extension likely
Start date: 1 October 2023 or soon thereafter
This is an exciting opportunity to help lead an ongoing programme of methodological research to tackle pressing global health problems in collaboration with leading international organisations.
The focus of this post is on the development of novel, flexible and computationally tractable spatio-temporal statistical inference tools in Bayesian Statistics and AI, and on their application in three domains. Applications range from HIV deep-sequence phylogenetics within the PANGEA-HIV consortium, to quantification and hotspot mapping of caregiver loss with the Global Reference Group for Children Affected by COVID-19 and in Crises, and species mapping and forecasting using oceanographic and climatological datasets.
You will have access to some of the finest longitudinal datasets in Africa and South America. Post holders will interact with a team of leading researchers. They will receive hands-on training in machine learning and modern statistics, epidemiological, and phylogenetic techniques, and will be mentored by leading scientists, who often publish in some of the top journals of the field.
Your base will be in the Department of Mathematics at Imperial College London, and you will work closely with the Machine Learning & Global Health Network (MLGH), a multi-institution research laboratory with members at Oxford, Imperial College London, University of Copenhagen, and Singapore. Post holders will be reporting directly to Dr Oliver Ratmann (Imperial), and collaborating closely with Professor Seth Flaxman (Oxford), Dr Kate Grabowski (Johns Hopkins), Dr Ettie Unwin (Bristol), Dr Adam Sykulski (Imperial), and Professor Christophe Fraser (Oxford).
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Research Associate in Modern Statistics, Global Health, and Conservation Ecology 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 Research Associate in Modern Statistics, Global Health, and Conservation Ecology
✨Tip Number 1
Network like a pro! Reach out to professionals in the field of statistics and global health on platforms like LinkedIn. Join relevant groups and engage in discussions to get your name out there.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills. Since this role involves Bayesian Statistics and AI, make sure you can confidently discuss your experience and knowledge in these areas.
✨Tip Number 3
Showcase your passion for global health and conservation ecology during interviews. Share any relevant projects or research you've been involved in that align with the job description.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Research Associate in Modern Statistics, Global Health, and Conservation Ecology
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of Research Associate. Highlight your experience in modern statistics, global health, and conservation ecology. We want to see how your skills align with the exciting projects we’re working on!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about this role and how your background makes you a perfect fit. Don’t forget to mention any relevant collaborations or research that excites you about our work.
Showcase Your Technical Skills: Since this role involves Bayesian Statistics and AI, be sure to highlight your technical skills in these areas. We love seeing examples of your work, so if you have any projects or publications, include them to demonstrate your expertise!
Apply Through Our Website: We encourage you to apply through our website for a smooth application process. It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at The International Society for Bayesian Analysis
✨Know Your Stats
Brush up on your knowledge of Bayesian statistics and spatio-temporal statistical inference tools. Be ready to discuss how you've applied these concepts in past projects or research, as this will show your understanding and relevance to the role.
✨Research the Team
Familiarise yourself with the work of Dr Oliver Ratmann and the other leading researchers you'll be collaborating with. Mentioning their recent publications or ongoing projects during the interview can demonstrate your genuine interest and initiative.
✨Prepare for Technical Questions
Expect technical questions related to machine learning and modern statistics. Practise explaining complex concepts in simple terms, as you may need to communicate these ideas to non-experts in the field.
✨Show Your Passion for Global Health
Be prepared to discuss why you're passionate about global health issues and conservation ecology. Share any relevant experiences or projects that highlight your commitment to making a difference in these areas.
