MPhil/PhD Studentship in Statistics and Data Science

Investing in excellence: studentship opportunities

The University of Derby has an opportunity for a full-time postgraduate research studentship in the Data Science area of research in the College of Science and Engineering (SE).

The successful applicant will receive a maintenance stipend (based on the minimum stipend defined by UKRI, currently £19,237 for the academic year 2024/25) and home MPhil/PhD tuition fees (£4,786 - subject to amendment) only up to the target submission date.

Please note: if your application is successful and you are assessed as international for fees purposes, you will need to pay the difference between the home fees and the international fees.

The intended intake period is March 2025.

The successful applicant will be expected to complete their MPhil/PhD within 3 years on the MPhil/PhD route, contribute to the College REF submission and get involved in the wider research activities of the College.

Applicants will become part of a friendly and welcoming team and will be supported and managed by their supervisors.

The vacancy details are as follows:

Project description

Complex systems are central to understanding many real-world challenges. Real-world data sets, such as road accidents or property transactions, often exhibit segmented behaviour where variance changes near critical points. These systems often exhibit fluctuating behaviours and critical transitions, with variance and skewness playing a critical role in their analysis.

Linear regression models often fail to account for key features like heteroscedasticity (variance that depends on the signal) and skewness, which can lead to biased model parameters. These issues also arise in other complex data analysis problems across areas such as public health, finance, and environmental science. For example, floods, which are becoming increasingly common due to climate change, and wind speeds, critical with the growing reliance on renewable energy, both require models with flexible distributions to account for their varying variance and asymmetry.

By improving these models, we can enhance our ability to predict future events and allocate resources more effectively to mitigate their impacts and protect communities. 

Purpose/objectives 

1. Segmented Time-Dependent Data Modelling: Develop models that allow for segmented relationship while accounting for skewness and heteroscedasticity over time. 
   
   
2. Expanding Distributional Flexibility: Enhance the Bayesian framework by incorporating a broader range of analytically intractable distributions. This will improve the framework’s ability to accurately represent complex datasets related to flooding and wind speed, thereby providing greater flexibility and accuracy in predictions. 
   
   
3. Develop Predictive Models: Use the expanded Bayesian framework and machine learning methods to develop predictive models for flood risk and wind dynamics. 

Potential project impact 

1. Improved Forecasting Accuracy 
2. Resource Allocation 
3. Climate Resilience 
4. Advancements in Statistical Modelling 

Principal accountabilities and responsibilities

In this PhD project, you will develop and apply an advanced Bayesian Generalised Modelling Framework (BGMF) that allows for segmentation and time dependency accounting for variance and skew. Additionally, you will expand the BGMF to include a wider range of analytically intractable distributions. This enhancement will improve the model’s ability to represent complex datasets related to flooding and wind speed, thereby offering greater flexibility and accuracy in predictions.

Finally, apply the enhanced BGMF along with machine learning methods, to real-world data provided by the Met Office (e.g., temperature, rainfall, sunshine, radiation, wind) and the Environment Agency (e.g., water level, flow, wind, temperature), with the aim of improving forecasting accuracy and optimising resource allocation to mitigate the impacts of these natural phenomena.

To apply

Please review our entry requirements before submitting your application and check out the 'Getting Started' section below.

Completed applications should be submitted via our online application system quoting funding reference: CoSE_PGRS_VocalBiomarkers_MAR23

Apply online

Closing dates for applications: 5pm, Monday 6 January 2025.

(Please note: we encourage applicants to apply as soon as possible as we reserve the right to close before 6 January 2025 if a high volume of applications is received.)

Interviews: 22 January to 23 January 2025.

If you have not been invited for an interview by the interview date, please assume your application has been unsuccessful.

For other enquiries which are subject-specific please contact:

Dr Jack Sutton - j.sutton@derby.ac.uk

Professor Farid Meziane - F.Meziane@derby.ac.uk

Find out more about our research degrees.

Getting started

Before you begin your application, make sure you have:

Studentship funding reference code

This is provided on the individual studentship advert and must be specified in your application. 

Personal statement

A 500-word personal statement outlining your suitability for the studentship project. This is a mandatory requirement and you must upload it into your application. You should include your reasons for applying for the studentship, your experience in the field, how you feel you would benefit from studying and relevant information about your previous studies.

Your CV

A CV outlining your academic and professional experience.

Qualifications

Your qualification details including grades and dates taken. You will have the opportunity to upload scanned copies of your qualification certificates and transcripts in the application. If you have no formal qualifications, you can also state this in your application.

Passport/birth certificate

A scanned copy of your passport or full birth certificate. This will help us verify your application to study with us. International applicants can provide a copy of their passport only for visa assessment purposes, and their current visa if residing within the UK.

Academic references

Two signed academic references. This is optional at application stage but highly encouraged. If successful in your application, two academic references will be a mandatory requirement of admission. The references should be in written format, signed and dated from either a supervisor or tutor from your most recent studies.