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Position: PhD Studentship in Statistical Science
Institution: University College London
Department: Statistical Science
Location: London, United Kingdom
Duties: This project will address the major contemporary challenges around model validation and success control in decision making, in: Theory of quantitative model comparison and automated model building, with a focus on heterogeneous, structured or hierarchical modelling tasks such as for panel or spatio-temporal data. Software engineering of automated modelling and model validation workflows implemented as modular toolbox environments in R and/or python. Deployment in real world applications in the energy, engineering, and health domains, and development of best practices and experimental validation principles
Requirements: The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class Bachelor’s degree, or a Master’s degree with merit or distinction in Mathematics, Statistics, Computer Science, Engineering or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable
   
Text: PhD Studentship in Statistical Science, - Ref:1751378 Click here to go back to search results UCL Department / Division Statistical Science Location of position London Duration of Studentship 4 years Stipend ?16,777 per annum plus tuition fees Vacancy Information Applications are invited for a PhD Industrial CASE studentship funded jointly by the EPSRC and Royal Dutch Shell . The studentship will commence in November 2018 and is based at the UCL Department of Statistical Science . To be eligible for the full award, applicants must normally be UK/EU nationals and have been ordinarily resident in the UK for 3 years prior to the start of the studentship. Further details concerning the regulations on student eligibility are available on the EPSRC website . Studentship Description With more and more machine learning and artificial intelligence technologies being adopted into decision making processes, success control and validation is becoming a matter of increasing concern for end users of modern data science. Of particular concern are reliable answers to questions such as ?does the new model perform better than the state-of-art one?? and ?is the artificial intelligence based decision making process really better than the standard one?, for which replicable, quantitative and generalizable automated testing workflows are sought. Closely related is the question of automated building meta-strategies for data integration, pipelining, and modelling including prediction making or quantification of uncertainty. This project will address the major contemporary challenges around model validation and success control in decision making, in: Theory of quantitative model comparison and automated model building, with a focus on heterogeneous, structured or hierarchical modelling tasks such as for panel or spatio-temporal data. Software engineering of automated modelling and model validation workflows implemented as modular toolbox environments in R and/or python. Deployment in real world applications in the energy, engineering, and health domains, and development of best practices and experimental validation principles. The studentship is 4 years in duration, with the student spending 3 months on placement at Shell. The award covers tuition fees plus a tax-free maintenance stipend, which is currently a minimum of ?16,777 per annum. The successful candidate will be also given options to additionally engage in consulting and internship opportunities with applied project partners. Person Specification The requirement for admission to the MPhil/PhD in Statistical Science is a 1st class or high upper 2nd class Bachelor?s degree, or a Master?s degree with merit or distinction in Mathematics, Statistics, Computer Science, Engineering or a related quantitative discipline. Overseas qualifications of an equivalent standard are also acceptable. Further details can be found on the Departmental website . We are specifically looking for applicants with an excellent joint background in practical data analytics, software engineering, as well as mathematics, statistics and machine learning theory. Candidates should also be enthusiastic about working in an interdisciplinary environment that accompanies the full translational process of directly deploying theoretical findings in a decision making context through solid software implementation, as well as about leveraging cutting edge applications for informing the development of interesting theoretical research avenues. Informal enquiries to Dr Franz Kir?ly are welcome. Eligibility Candidates should apply for the Research Degree: Statistical Science (RRDSTASING01) in the usual way by completing the online form and, in addition (and very importantly), send a separate covering letter directly to the department making their case for the funding. The covering letter should be sent to Dr Russell Evans at email address below. Applications will be considered on a rolling basis, the first batch on 16 September, until the studentship is filled (i.e. the below closing date represents only a final deadline). You are therefore advised to apply as soon as possible. Contact name Dr Russell Evans Contact details stats.pgr-admissions@ucl.ac.uk UCL Taking Action for Equality Closing Date 31 Oct 2018 Latest time for the submission of applications 23.59 Studentship Start Date November 2018
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