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- Tasks: Develop innovative causal inference tools for single-cell sequencing data.
- Company: Join Imperial College London and Cambridge, leading institutions in research and innovation.
- Benefits: Enjoy a generous travel budget and collaborative opportunities with top experts.
- Why this job: Make a real impact on neurological disease research while working with cutting-edge technology.
- Qualifications: Strong background in Bayesian statistics, computational statistics, or machine learning required.
- Other info: Position funded for 3 years with potential for extension; informal enquiries welcome.
The predicted salary is between 28800 - 48000 £ per year.
Research Associate in Bayesian Statistics and Causal Inference, Imperial College London and Cambridge, UK
Feb 15, 2023
*Key dates*:
· Closing date 27 February 2023
· Interviews will be held a week after the closing date of the application deadline
We are seeking a Research Associate in Bayesian Statistics and Causal Inference with a strong background in Bayesian and computational statistics or machine learning and causal inference, including machine and statistical causal structure learning, to develop novel causal inference tools tailored for single-cell sequencing data.
This project is based on the world-largest single-cell RNA-sequencing dataset of the human brain derived from 147 samples combined with genotype information to define molecular causes for neurological disease. It will also expand and use other publicly available single-cell datasets combined with genotype data. The main aim of this project consists of novel causal inference and structure learning methodologies as well as their software implementation tailored to, but not limited, to scRNA-seq.
This is a collaborative project with national and international experts in their field including Prof Michael Johnson, Professor of Neurology and Genomic Medicine, Imperial College, Dr Leonardo Bottolo, Reader in Statistics for Biomedicine, University of Cambridge, and Prof Guido Consonni, Professor of Statistics, Universita’ Cattolica del Sacro Cuore, Milan, Italy. The position is funded by the “MRC Better Methods, Better Research” panel and includes a generous travel and computing budget. The funds for this post are available initially for 3 years in the first instance.
Informal enquiries may be made to Dr Verena Zuber at v.zuber@imperial.ac.uk or Dr Leonardo Bottolo at lb664@cam.ac.uk
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Research Associate in Bayesian Statistics and Causal Inference, Imperial College London and Cam[...] 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 Bayesian Statistics and Causal Inference, Imperial College London and Cam[...]
✨Tip Number 1
Network with professionals in the field of Bayesian statistics and causal inference. Attend relevant conferences or webinars where you can meet experts like Prof Michael Johnson or Dr Leonardo Bottolo, as they may provide insights or even recommend candidates for the position.
✨Tip Number 2
Familiarise yourself with the latest research and methodologies in causal inference and single-cell RNA-sequencing. Being well-versed in current trends will not only boost your confidence but also allow you to engage in meaningful discussions during interviews.
✨Tip Number 3
Prepare to discuss your previous projects that involved Bayesian statistics or machine learning. Be ready to explain your thought process, the challenges you faced, and how you overcame them, as this will demonstrate your problem-solving skills and expertise.
✨Tip Number 4
Reach out to the contacts provided in the job description for informal enquiries. This shows initiative and genuine interest in the role, and it could give you an edge over other candidates by establishing a personal connection with the hiring team.
We think you need these skills to ace Research Associate in Bayesian Statistics and Causal Inference, Imperial College London and Cam[...]
Some tips for your application 🫡
Understand the Role: Familiarise yourself with the specifics of the Research Associate position in Bayesian Statistics and Causal Inference. Highlight your relevant experience in Bayesian and computational statistics, machine learning, and causal inference in your application.
Tailor Your CV: Make sure your CV reflects your skills and experiences that are directly related to the job description. Emphasise any previous work with single-cell sequencing data or similar projects, as well as your collaboration with experts in the field.
Craft a Compelling Cover Letter: Write a cover letter that not only outlines your qualifications but also demonstrates your enthusiasm for the project. Mention how your background aligns with the goals of the research and your interest in contributing to the development of novel causal inference tools.
Proofread and Submit: Before submitting your application, carefully proofread all documents for clarity and correctness. Ensure that you follow the submission guidelines provided by Imperial College London and submit your application through our website before the closing date.
How to prepare for a job interview at The International Society for Bayesian Analysis
✨Showcase Your Technical Skills
Make sure to highlight your expertise in Bayesian statistics and causal inference during the interview. Be prepared to discuss specific projects or experiences where you applied these skills, especially in relation to single-cell sequencing data.
✨Familiarise Yourself with the Research Team
Research the backgrounds of the professors and experts involved in the project. Understanding their work and how it relates to your potential role will demonstrate your genuine interest and help you engage in meaningful discussions.
✨Prepare for Problem-Solving Questions
Expect questions that assess your problem-solving abilities in statistical modelling and machine learning. Practice articulating your thought process clearly, as this will showcase your analytical skills and ability to tackle complex challenges.
✨Ask Insightful Questions
Prepare thoughtful questions about the project, such as the methodologies they plan to use or the datasets involved. This not only shows your enthusiasm but also your critical thinking regarding the research objectives.
