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Research Officer (DASS)
Company Information
The Department is home to internationally respected experts in statistics and data science. Maintaining and advancing our leading reputation for teaching and research is our top priority. At the same time, we aim to provide a supportive, friendly and informal environment for all students and staff.
The Department has grown in recent years, especially in the area of data science which is a key priority for education and research in the LSE2030 strategy. We aim to play a leading role in fostering the study of data science and new forms of data, with a focus on its social, economic, and political aspects.
We offer a vibrant research environment with specialisms in four main areas: data science, probability in finance and insurance, social statistics, and time series and statistical learning.
The appointee will conduct the research for EPSRC programme grant “Statistical Foundations for
Detecting Anomalous Structure in Stream Settings (DASS)” under the direction of Professor Qiwei
Yao.
Duties include:
• Conducting research projects or programmes either independently or in a team.
• Demonstrating the ability to analyse and research complex ideas, concepts or theories and applying appropriate methodologies.
• Designing and conducting numerical work with both simulated and real data.
• Contributing to the formulation of peer reviewed research grant proposals.
• Writing up research for publication in a variety of modes including peer reviewed journals.
• Initiating and sustaining links with external bodies to foster collaboration.
• Presenting research papers at conferences.
• Organising conferences, seminars and workshops.
• Contributing creative solutions to research challenges.
Candidates must have a PhD in statistics or a closely-related subject or expect to have submitted their PhD by the post start date. Throughout, you should have demonstrated an ability to develop new statistical methods or theory in one of the relevant areas, including but not limited to: anomaly detection; changepoint analysis; non-stationary time series analysis, high dimensional statistics, statistical-computational tradeoffs, scalable statistical methods. You will also have shown a demonstrable ability to produce academic writing of the highest publishable quality.