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小木虫论坛-学术科研互动平台 » 科研生活区 » 导师招生 » 博士招生 » 澳大利亚斯威本科技大学全奖博士招生 (Big Data/Data Analytics/Deep Learning)
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qfred008

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[交流] 澳大利亚斯威本科技大学全奖博士招生 (Big Data/Data Analytics/Deep Learning)

?????????????????ó?????ì?ò????ר??Prof Timos Sellis (https://scholar.google.com.au/ci ... AAAJ&hl=en)??????Sellis????????°??ó????????±??????ó?§(Swinburne University of Technology )???????§?????????¤??

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Australia-Israel PhD Research Scholarship (co-funded by Swinburne University of Technology: https://www.swinburne.edu.au/ and the Trawalla Foundation: https://www.trawallafoundation.com.au/)

The "Australia-Israel PhD Research Scholarship" program aims at strengthening relations between Australia and Israel, by fostering innovation and academic exchange. The scheme will support a total of two Partnered PhD candidates starting their PhD in 2017.  Each candidate will receive:
* A full tuition fee scholarship for 4 years
* A stipend of AUD 30,000 per year for 3 years
* Funding towards international air fare travel to Tel Aviv University to complete part of candidature

Eligibility/Selection Criteria:
To become and remain eligible for the Australia-Israel PhD research scholarship, a prospective candidate must:
* Have completed at least four years (or equivalent) of tertiary education studies in computer science, computer engineering or related discipline at a high level of achievement (75% or higher).
* Have good understanding of data management; some knowledge of statistics is preferred.
* Fulfill the PhD candidature entry requirements of both Swinburne University of Technology and Tel Aviv University including language proficiency.
* Not have previously held a postgraduate research scholarship from Swinburne University of Technology.

Successful applicants will be required to:
* Enroll as a full time student in the PhD program of Swinburne University of Technology.
* Spend 12 months at Tel Aviv University under joint supervision of Tel Aviv University and Swinburne University of Technology academics.

The applicants will work in one of the PhD projects outlined below:
* Digital Humanities: Digital Humanities is an emerging, interdisciplinary area of research which looks to enhance and redefine traditional humanities scholarship through digital means.  The ability to scan huge volumes of material, to search specific data and establish connectivity between different bodies of knowledge by either connecting metadata from several institutions sometimes using semantic linking mechanisms (powered by SKOS or RDF), or by culling statistical data in order to acquire quantitative results, has turned the benefits of digital technologies from being just "work and time savers" to tools that bring about significant qualitative results, and opening up new fields of research and thought.
* Deep Learning for Question-Answer Systems: Assembling useful Question-Answer (QA) repositories out of operational systems (eg. Customer care lines) is a very hot area. The QA pairs may come from different types of resources, i.e. forums and social media discussions, email inquiries, and customer service recordings & documents. Furthermore, different types of resources may use different terminology/expressions to depict the same question and/or answer. Deep learning is a powerful technique not relying on hand-crafted features (which are less data-driven in terms of feature representation) that other methods use. Deep learning is also very good at knowledge transfer. For example, a QA deep network modeled on email inquiries may be learnt together with other QA deep networks modeled on other resources such as customer service recordings & documents under a multi-task transfer learning framework.
* Analysing product development, manufacturing systems, and business models data to improve Industry 4.0 platforms: Industry 4.0 solutions bring together cyber, physical and human systems that work synergistically towards new manufacturing platforms. These systems generate their own data captured typically via IoT and Cloud infrastructures. Analytics becomes very important in this context as it is the primary means of extracting insights, forecasting and suggesting alternatives. The project will focus on building a Data Incubator (DI) for Industry 4.0. A DI is a data platform that links industry, government, and research, by efficiently leveraging the potential of Big Data, through access and deployment of the appropriate resources and available tools. The DI can provide an isolated environment for industry to incubate and commercialise innovative data-driven solutions. It will provide best of- breed technology in Big Data ingestion, management and analysis, predictive analytics, and data visualisation and will accelerate innovation for Industry 4.0. Example analytics solutions that will be studied include: 1) Determining patterns in major and minor stoppages in production lines. Studying if major and minor stoppages can be predicted with a high level of accuracy using collected data; 2) Complex flaw detection and prediction; correlating data across machines and processes; and 3) Real-time IoT data analysis; synthesizing data with different time, scale, noise, uncertainty characteristics.
* Graph Streaming Data Analytics: Streaming graph data are very common nowadays especially due to the emergence of social network platforms. Graph streaming algorithms have emerged as a paradigm for analyzing massive datasets in which there are limited memory resources available. Many graph problems require novel solutions to be devised in a streaming environment. For example, dense subgraphs indicate interesting structures in many real-world graphs, including social networks, web-link graphs, and biological networks. The Densest Subgraph problem has emerged as a key computational question in datasets best modeled as graphs. Another commonly found example is community detection, where communities of nodes with particular characteristics are sought. This project aims at studying new streaming algorithms for a variety of known graph problems, with special emphasis on the effectiveness, efficiency and scalability of algorithms.

To express interest for these positions, please send by email to Prof Timos Sellis: tsellis@swin.edu.au
* A current CV
* Transcripts of all previous qualifications (BSc, MSc, etc)
* A research proposal on one of the above projects, up to 5 A4 pages, describing the research questions and general methodology in addressing them. You can view the above areas as general areas of research and you can suggest specific research topics that you find interesting.
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Rccccc

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可以说一下语言成绩要求吗
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qfred008

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大家如果有兴趣和疑问，可以给我留言。
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2楼2017-03-31 12:18:26
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沧海小海

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东楚游侠
化学专业的可以申请吗？对数据分析很感兴趣

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xiwangzijihaoqilai
3楼2017-03-31 15:47:39
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qfred008

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3脗楼: Originally posted by 虏脳潞拢脨隆潞拢 at 2017-03-31 17:47:39
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禄炉脩搂脳篓脪碌脩搂脝冒脌麓禄谩卤脠陆脧鲁脭脕娄
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4楼2017-04-04 10:58:18
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paopao

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软件工程专业硕士今年毕业，在准备英语，想问下这个职位的deadline

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6楼2017-04-04 15:51:25
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qfred008

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5脗楼: Originally posted by Rccccc at 2017-04-04 13:57:28
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International candidates must meet one of the following requirements:

Obtain a minimum IELTS overall band of 6.5 (Academic Module) with no individual band below 6.0 or a TOEFL iBT (Internet-based) minimum score of 79 (with a reading band no less than 18 and writing band no less than 20); or Pearson (PTE) 58 (no communicative skills less than 50) no longer than 24 months before submitting your application

Satisfactorily complete the Swinburne College English for Academic Purposes (EAP) Advanced level certificate at the postgraduate level (EAP 5: PG-70%)

Successfully complete a total of 24 months (full time equivalent) of formal study where the language of instruction and assessment was English at AQF level 7 or above (or equivalent) at an approved university no longer than 60 months before submitting your application.
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7楼2017-04-06 12:28:07
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qfred008

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6脗楼: Originally posted by paopao at 2017-04-04 17:51:25
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8楼2017-04-06 15:33:30
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paopao

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8楼: Originally posted by qfred008 at 2017-04-06 15:33:30
越快能入职越好...

还在考托福

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9楼2017-04-06 18:12:15
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gqcumt2015


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10楼2017-04-06 18:46:45
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