Data Mining and Knowledge Discovery for complex industrial processes within ... The cross-disciplinary application of ma
COLLABORATE MENTOR DELIVER
PhD Scholarship Opportunity within the ARC Research Hub for Australian Steel Manufacturing The Steel Research Hub will be the centerpiece for collaborative steel research in Australia, striving to deliver breakthrough product and process innovations that will enable the Australian Steel Industry to compete on a global stage. For more on the Steel Research Hub visit our website at: http://steelresearchhub.uow.edu.au One of the programs in the hub, “Sustainable Steel Manufacturing”, is seeking an enthusiastic and high-achieving student, committed to pursuing a challenging PhD project in Data Mining and Knowledge Discovery for complex industrial processes within the domain of Steel Manufacturing. The discovery of patterns in data is the cornerstone of Data Mining; sometimes for predicting the future, and other times for extracting meaning. The ability to extract knowledge from data has been the driving force of Data Mining since its inception. Data sources from such areas generally involve complex multidimensional forms, such as numerous parallel streams of time series data, combined with an abundant collection of spatiotemporal features. An overall objective of this research is to discover and characterise potential modalities of these processes and to further combine these in a manner to facilitate overall holistic models of their behaviours and their interacting components. The Steel Research Hub is committed to industry-relevant outcomes; therefore, the successful candidate will be expected to work closely with industry-based researchers. Required knowledge, skills and experience are as follows: 1. 2.
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A bachelors honours degree, with a first-class or second-class upper division standing, in electrical or computer engineering, computer science, statistics, or related fields from a reputable institution; A strong computational background and programming skills are essential with demonstrated proficiency in one or more analysis packages such as, Matlab or R. Experience with one or more high level general purpose programming languages, such as C/C++, Python or Java and prior experience with ‘big data’ analytics tools would also be desirable; Any prior experience in the following areas is desirable but not essential: a. The cross-disciplinary application of machine learning, data mining and related techniques such as digital signal processing within complex domains. b. Experience and demonstrated abilities with Bayesian Networks, Gaussian Mixture Models, Time-series analysis, Spatial or Spatiotemporal data mining. c. Some previous research experience, especially in multidisciplinary areas. Excellent interpersonal, communication and written presentation skills. Interested candidates should forward their Curriculum Vitae, including any publications and three references, as a single PDF file, Dr David Stirling (
[email protected]) Closing date: Open until 28/02/2017 unless filled prior with a suitable candidate. ARC RESEARCH HUB FOR AUSTRALIAN STEEL MANUFACTURING
