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- Tasks: Conduct cutting-edge research in machine learning and spatial statistics at The Alan Turing Institute.
- Company: Join the University of Warwick, a leading institution known for innovation and research excellence.
- Benefits: Enjoy a competitive salary, London allowance, and opportunities for professional growth.
- Why this job: Be part of a dynamic team tackling real-world challenges in air quality and data science.
- Qualifications: PhD in Statistics, Computer Science, or Applied Mathematics; strong background in Bayesian inference preferred.
- Other info: Fixed term position for 24 months with potential for promotion upon PhD completion.
The predicted salary is between 29799 - 38833 £ per year.
An enthusiastic individual is sought as a postdoctoral research fellow, to work in the area of machine learning and spatial statistics. The post is a joint appointment between the Departments of Computer Science and Statistics at the University of Warwick, and the successful application will be based at The Alan Turing Institute in London (a London allowance will be payable).
You will join a team of researchers affiliated with the ATI and led by Dr Theo Damoulas, including research assistants and PhD students in computer science and statistics. You will be expected to perform high quality research under the supervision of Dr. Theo Damoulas and Prof. Mark Steel, as part of the Turing-Lloyds Register Foundation funded project ‘Air Quality Sensor Networks’. This project is likely to involve hierarchical Bayesian models, nonparametric Bayesian inference, graphical models, active learning, experimental design and issues in spatiotemporal inference such as non-stationarity and non-separability.
The expectation is that you will produce breakthrough research results in the areas of sensor placement, high-resolution space-time forecasting, dynamic modelling, and contribute to publishing these results in top rated venues.
You will possess a PhD or an equivalent qualification in Statistics or Computer Science or Applied Mathematics (or you will shortly be obtaining it). You should have a strong background in one or more of the following areas: Bayesian inference, spatial statistics, probabilistic machine learning.
If you have not yet been awarded your PhD but are near submission or have recently submitted your PhD, any offers of employment will be made as Research Assistant on level 5 of the University grade structure (£28,936). Upon successful award of your PhD, you will be promoted to Research Fellow on the first point of the level 6 of the University grade structure (£29,799).
Salary: £29,799 – £38,833 per annum (plus £3000 London Allowance)
Location: The Alan Turing Institute, London
Fixed Term Position for 24 months
The start date is to be negotiated with the successful candidate, but is expected to be no later than January 2019
Closing date: 12 July 2018
Informal enquires can be addressed to Dr. Theo Damoulas (T.Damoulas@warwick.ac.uk) or Professor Mark Steel (M.Steel@warwick.ac.uk).
Research Fellow in machine learning and spatial statistics employer: The International Society for Bayesian Analysis
Contact Detail:
The International Society for Bayesian Analysis Recruiting Team
T.Damoulas@warwick.ac.uk
StudySmarter Expert Advice 🤫
We think this is how you could land Research Fellow in machine learning and spatial statistics
✨Tip Number 1
Network with current researchers in the field of machine learning and spatial statistics. Attend relevant conferences or seminars where you can meet professionals, including those from the University of Warwick and The Alan Turing Institute. This can help you gain insights into the research culture and potentially get a referral.
✨Tip Number 2
Familiarise yourself with the specific projects being undertaken by Dr. Theo Damoulas and Prof. Mark Steel. Understanding their research focus will allow you to tailor your discussions and demonstrate your genuine interest in contributing to their work during any interviews or informal chats.
✨Tip Number 3
Engage with the academic community by publishing your own research or collaborating on projects related to Bayesian inference and spatial statistics. Having a strong publication record can significantly enhance your profile and show your commitment to advancing knowledge in these areas.
✨Tip Number 4
Prepare for potential interviews by brushing up on key concepts in hierarchical Bayesian models and nonparametric Bayesian inference. Be ready to discuss how your previous research aligns with the goals of the project, particularly in sensor placement and high-resolution space-time forecasting.
We think you need these skills to ace Research Fellow in machine learning and spatial statistics
Some tips for your application 🫡
Understand the Role: Read the job description thoroughly to grasp the expectations and requirements for the Research Fellow position. Familiarise yourself with the specific areas of research, such as Bayesian inference and spatial statistics, to tailor your application accordingly.
Highlight Relevant Experience: In your CV and cover letter, emphasise your experience in machine learning, spatial statistics, and any relevant projects or research. Be specific about your contributions and outcomes to demonstrate your capability for high-quality research.
Craft a Strong Cover Letter: Write a compelling cover letter that outlines your motivation for applying, your research interests, and how they align with the project at The Alan Turing Institute. Mention your familiarity with hierarchical Bayesian models and other relevant methodologies.
Proofread Your Application: Before submitting, carefully proofread your application materials for clarity, grammar, and spelling errors. A polished application reflects your attention to detail and professionalism, which are crucial in academic roles.
How to prepare for a job interview at The International Society for Bayesian Analysis
✨Know Your Research
Familiarise yourself with the latest developments in machine learning and spatial statistics. Be prepared to discuss your own research and how it aligns with the projects at The Alan Turing Institute, especially the 'Air Quality Sensor Networks' project.
✨Demonstrate Technical Skills
Be ready to showcase your expertise in Bayesian inference, spatial statistics, and probabilistic machine learning. You might be asked to solve problems or explain concepts, so brush up on relevant techniques and methodologies.
✨Engage with the Team
Since you'll be working closely with Dr. Theo Damoulas and Prof. Mark Steel, express your enthusiasm for collaboration. Mention any previous experiences where teamwork led to successful outcomes in research.
✨Prepare Questions
Have insightful questions ready about the research environment, ongoing projects, and expectations for the role. This shows your genuine interest in the position and helps you assess if it's the right fit for you.
