Fully Funded EPSRC And Swansea University PhD Scholarship

The project funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnerships (DTP) and Swansea University’s Faculty of Science and Engineering, presents an exciting opportunity for prospective doctoral candidates in the fields of Computer Science, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning and Neuro Symbolic Computing.

This initiative seeks to integrate symbolic computing with machine learning models to advance the understanding of the mathematical modeling of real world systems. Led by Dr. Xiaochun Cheng and Professor Xianghua Xie, the research aims to contribute to cutting edge developments in AI knowledge discovery computing.

Project Timeline and Enrollment

The project will commence on October 1, 2024, with enrollments opening in mid September. Subsequent enrollment periods are scheduled for January 1, 2025, April 1, 2025 and July 1, 2025. The flexibility in enrollment dates accommodates prospective candidates’ preferences, allowing them to embark on this intellectual journey at a time that suits their schedules.

Supervisors

The project will be overseen by two distinguished supervisors: Dr. Xiaochun Cheng (contact: [email protected]) and Professor Xianghua Xie. Both supervisors bring a wealth of experience and expertise in the field of AI ensuring that the PhD candidate receives comprehensive guidance throughout the research process.

Program of Study

Aligned with the project’s objectives, the PhD program focuses on Computer Science, offering a unique opportunity for full time or part time study. The program encompasses a dynamic curriculum that combines elements of Machine Learning, Mathematical Modeling and Knowledge Discovery. This interdisciplinary approach aims to bridge the gap between data driven insights and mathematical modeling, pushing the boundaries of these fields.

Project Description

The core objective of the research is to unravel the governing mathematical models of real world systems. The project proposes the integration of symbolic computing with machine learning models, creating a novel approach termed AI knowledge discovery computing for mathematical modeling applications. This pioneering research is expected to contribute significantly to the advancement of AI knowledge discovery computing.

Significance of the Project

The successful candidate will engage in groundbreaking work that connects data driven insights with mathematical modeling. This endeavor holds the potential for AI computing breakthroughs, particularly in data intensive applications. By pushing the frontiers of knowledge discovery, the project seeks to make a lasting impact on the field of AI and contribute to the broader scientific community.

Research Environment

The PhD candidate will be part of a dynamic research team led by experts in AI. This collaborative environment provides access to state of the art research resources and training ensuring that the candidate is equipped with the necessary tools to excel in their research endeavors. The emphasis on collaboration extends to working with various experts enhancing the overall research experience.

Professional Development

The project is committed to fostering the professional growth of its candidates. In addition to the research focused curriculum, the PhD candidate will have the opportunity to attend workshops, conferences and networking events. These activities are designed to build transferable skills and enhance career prospects, aligning with the project’s commitment to nurturing well rounded researchers.

Eligibility Criteria

To be eligible for this PhD studentship, candidates must hold an undergraduate degree at the 2.1 level in Computer Science, Mathematics or a closely related discipline. Alternatively, candidates with an appropriate master’s degree with a minimum overall grade at ‘Merit’ are also eligible. English language proficiency, as demonstrated by an IELTS score of 6.5 overall (with no individual component below 6.0) or an equivalent recognized by Swansea University, is required.

Desirable Qualities

The ideal candidate should possess a strong background in computer science, AI and machine learning. A passion for Machine Learning, Mathematical Modeling and Knowledge Discovery is essential, coupled with excellent analytical and problem solving skills. This scholarship is open to candidates of any nationality, reflecting the project’s commitment to diversity and inclusivity.

Funding and Application

The EPSRC funded studentships cover the full cost of tuition fees and provide an annual stipend at the UKRI rate, currently set at £18,622 for the academic year 2023/24. Additionally, successful candidates will have access to additional research expenses. International applicants are encouraged to apply and up to 30% of the cohort can comprise international students. Once this limit is reached, offers to international students may no longer be extended.

Application Process and ATAS:

Applications from international candidates are still being accepted and international students will not be charged the fee difference between the UK and international rate. Applicants should satisfy the UKRI eligibility requirements. While the program does not require ATAS clearance as part of the scholarship application process, successful award winners will be provided with details on how to apply for ATAS clearance in tandem with the scholarship course offer.

In conclusion, this EPSRC funded project at Swansea University offers a unique and exciting opportunity for prospective PhD candidates to engage in cutting edge research at the intersection of AI, machine learning and mathematical modeling.

The collaborative research environment, experienced supervision and commitment to professional development make this project an ideal platform for those passionate about advancing the frontiers of knowledge in these fields. Interested candidates are encouraged to explore the eligibility criteria and application process, recognizing the potential for groundbreaking contributions to the field of AI knowledge discovery computing.

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