Funded PhD Opportunity

Autonomous Decision Analytics by Integrating Machine Learning and Symbolic Reasoning

Subject: Computer Science and Informatics


Summary

Background and Challenges

Breakthroughs in artificial intelligence (AI) combined with data analytics and high-performance computing has seen the emergence of society-changing AI applications. However, autonomous decision-making and the black-box design of machine learning make the adoption of AI systems complicated and has led to a resurgence in interest in explainability of AI systems. The challenges emphasize the importance of work that ensures that algorithms are not merely efficient and accurate, but transparent and explainable. The research hypothesis is to investigate whether autonomous decision-making can be conducted in a complementary way through constructing and integrating a group of advanced models in order to achieve flexibility and predictivity to fit with different scenarios and improved transparency and enable explanation. Successful decision making analysis must combine the best qualities of both human analysts and computers, especially in high-level planning. This reflects an integration of two most fundamental human cognitive abilities: the ability to learn from the environment, and the ability to reason from what has been learned.

Aim

This research is focused on reconciling the advantages of robust learning in neural networks and reasoning and interpretability of symbolic representation. The aim of this project is to develop an autonomous decision-making system with a unified framework and methods that supports symbolic learning and reasoning using neural networks, and capable of bridging lower-level information processing (for perception and pattern recognition) and higher-level abstract knowledge (for reasoning and explanation).

Anticipated Outcomes/Impact This project provides an opportunity to combine cutting edge research at the intersection of knowledge representation and machine learning to address the above key challenges. The timeliness of this PhD project becomes also apparent in the potential of the above integration to contribute to the long-standing goal of explainable and interpretable AI. We envision the application of these methods to challenging problems arising in logistics, risk-based decision making, planning, and common-sense reasoning.


Essential criteria

  • Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC)
  • Sound understanding of subject area as evidenced by a comprehensive research proposal

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

  • Publications record appropriate to career stage
  • A comprehensive and articulate personal statement
  • Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

Funding

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Vice Chancellors Research Studentship (VCRS)

    Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Bursary (VCRB)

    Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    Fees only award (PhD fees + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. This scholarship also comes with £900 per annum for three years as a research training studentship grant (RTSG) allocation to help support the PhD researcher.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University


Reviews

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

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Key dates

Submission deadline
Friday 7 February 2020

Interview Date
Late March 2020


Applying

Apply Online  


Campus

Jordanstown campus

Jordanstown campus
The largest of Ulster's campuses


Contact supervisor

Dr Jun Liu


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