|Funding for:||UK Students, EU Students, International Students|
|Funding amount:||£19,528 Stipend.Tuition fee: £4,596 pa.|
|Placed On:||30th March 2023|
|Closes:||1st May 2023|
The position offered is for three and a half years full-time study. The current (2022-23) value of the award is stipend; £19,528 pa; tuition fee: £4,596 pa. Awards are usually incremented on 1 October each following year. The studentship will fund an international student with exceptional academic potential and academic record.
We will consider applications from students wishing to start during the 2022-23 academic year or who wish to begin their studies in autumn 2023.
The PhD project topic is to study and develop probabilistic and quantile loss approaches for learning from data streams and making short-term predictions under uncertainty, as well as using these predictions for risk-aware decision support and control optimization. The outcomes will be assessed and used in smart building applications with continuously generated sensor data. An initial focus will be the University of Birmingham’s smart campus, that is generating a significant amount of data. The goal is to estimate and optimize building running cost and related energy consumption in consideration of data uncertainty caused by human behaviours, environmental factors, etc. The PhD will be jointly supervised by Siemens and the University of Birmingham, based at the University but with opportunities for secondment to Siemens’ data research groups in Germany.
The applicant should hold a mathematics or computer science degree; have experience in machine learning (ideally in time series forecasting, uncertainty estimation or continual learning), and be proficient in Python/Java programming.
First or Upper Second Class Honours undergraduate degree and/or postgraduate degree with Distinction (or an international equivalent). We also consider applicants from diverse backgrounds that have provided them with equally rich relevant experience and knowledge. Full-time and part-time study modes are available.
We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled and neuro-diverse candidates. We also welcome applications for part-time study.
Supervisory team: Dr Shuo Wang (firstname.lastname@example.org), Dr Grant Wilson, Dr Holger Schoener.
To apply, please click on the ‘Apply’ button above
Type / Role: