Computer Science Fully Funded PhD Scholarship: Elevating Comparative Judgement Using a Large-Scale Human-In-The-Loop Bayesian Active Learning Approach (Ecstatic)

Job title:

Computer Science Fully Funded PhD Scholarship: Elevating Comparative Judgement Using a Large-Scale Human-In-The-Loop Bayesian Active Learning Approach (Ecstatic)

Company

Swansea University

Job description

Job Information Organisation/CompanySwansea University DepartmentCentral Research FieldComputer science » Other Researcher ProfileFirst Stage Researcher (R1) CountryUnited Kingdom Application Deadline1 May 2024 – 23:59 (Europe/London) Type of ContractOther Job StatusFull-time Hours Per Week35 Offer Starting Date1 Oct 2024 Is the job funded through the EU Research Framework Programme?Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure?NoOffer DescriptionComputer Science: Fully Funded PhD Scholarship: Elevating Comparative Judgement Using a Large-Scale Human-In-The-Loop Bayesian Active Learning Approach (Ecstatic)The Comparative Judgment (CJ) method, which has gained traction in UK schools over the past decade, involves assessors choosing the superior submission from a pair, rather than assigning a score to each one. This approach is less taxing for assessors and maintains accuracy for a small number of submissions. Recently, we developed a Bayesian active learning approach for CJ (BCJ; ), to solve a crucial problem of interaction-efficient pair selection while producing reliable estimations of ranks and predictive uncertainty.In this related project, for the first time, we will aim to scale BCJ to handle thousands of items (as opposed to tens of them), enabling ranking and scoring across schools and assignments. We will propose new methods to dynamically incorporate new items for ranking in BCJ in an interaction-efficient manner, and devise avenues for providing individual learners insight into their progress over time compared to their peers. We will evaluate these methods to establish their efficacy in helping assessors make informed decisions under uncertainty arising from the practical paucity of data and interactions, as well as better informing learners. These methods will be designed in collaboration with assessors and learners to ensure that they remain relevant and useful beyond the project completion.RequirementsResearch Field Computer science » Other Education Level Bachelor Degree or equivalentSkills/QualificationsCandidates must hold an Upper Second Class (2.1) honours degree or an appropriate master’s degree with a minimum overall grade at ‘Merit’ in Computer Science, Mathematics or a closely related discipline. If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements. Please note that you may need to provide evidence of your English Language proficiency.Specific RequirementsEnglish Language: IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent.Desirable skills and attributes:

• Excellent numerical and progamming skills;
• Knowledge of Python.;
• Knowledge of Bayesian statistics, machine learning, and optimisation, or a willingness to learn.

This scholarship is open to candidates of any nationality.Additional InformationBenefitsThis scholarship covers the full cost of tuition fees and an annual stipend at £19,237.Additional research expenses will also be available.Eligibility criteriaCandidates must hold an Upper Second Class (2.1) honours degree or an appropriate master’s degree with a minimum overall grade at ‘Merit’ in Computer Science, Mathematics or a closely related discipline. If you are eligible to apply for the scholarship (i.e. a student who is eligible to pay the UK rate of tuition fees) but do not hold a UK degree, you can check our comparison entry requirements. Please note that you may need to provide evidence of your English Language proficiency.English Language: IELTS 6.5 Overall (with no individual component below 6.0) or Swansea University recognised equivalent.Desirable skills and attributes:

• Excellent numerical and progamming skills;
• Knowledge of Python.;
• Knowledge of Bayesian statistics, machine learning, and optimisation, or a willingness to learn.

This scholarship is open to candidates of any nationality.Selection processPlease see our website for more information.Website for additional job detailsWork Location(s)Number of offers available 1 Company/Institute Swansea University Country United Kingdom City Swansea Postal Code SA2 8PP GeofieldWhere to apply WebsiteContact CitySwansea WebsiteStreetSingleton Park Postal CodeSA28PP E-Maila.a.m.rahat@swansea.ac.ukSTATUS: EXPIRED

Expected salary

£19237 per year

Location

Swansea

Job date

Fri, 05 Apr 2024 06:46:30 GMT

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