NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY -NTNU

PhD Fellowship in Time-Series Modelling of Financial Data

Location
Trondheim, Norway
Posted
07 Jun 2018
End of advertisement period
15 Aug 2018
Ref
IE 128-2018
Contract Type
Fixed Term
Hours
Full Time

Faculty of Information Technology and Electrical Engineering
Department of Engineering Cybernetics

NTNU’s Faculty of Information Technology and Electrical Engineering hereby invites applications for a fellowship within time-series modelling affiliated with the Department of Engineering Cybernetics. The Fellowship is financed by the Norwegian bank DNB and is one of three PhD positions in a strategic collaboration between the faculty and DNB.

As part of their strategic partnership, DNB and NTNU are funding 1 Postdoc and 3 PhD positions in a joint R&I project focusing on research in Big Data analytics and machine learning. With this project, the two parties will create an attractive environment for education, research and innovation, executed in close collaboration with the ongoing research and innovation efforts at the Telenor-NTNU AI-Lab.

By responding to this announcement you may become part of Norway’s strongest reseach community within the field of Artificial Intelligence, Big Data analytics and IoT. The Project’s activities will demonstrate how these technologies can be applied to solve business challenges within the banking and finance sector in particular, as well as for society in general.

The project effort involves technologies and architectures for dealing with large-scale realtime data processing, as well as the use of machine learning to uncover hidden knowledge in data and build intelligent banking applications. The intention is that the Project will increase the general capacity and quality of Big Data & Artificial Intelligence education and research in Norway and encourage the use of this technology in the banking and finance sector.

About the department

The Department of Engineering Cybernetics (Institutt for teknisk kybernetikk, ITK) has 25 professors, 17 adjunct professors, about 15 postdocs and researchers as well as 70 PhD candidates. Approximately 160 candidates graduate annually from the three MSc programs in cybernetics, which comprise over 700 students in total. Also, about 35 candidates graduate annually from the BSc study in electrical engineering with specialization in automation.

The research and educational activities at ITK include both fundamental and applied activities in areas such as automatic control and systems theory; estimation and optimization; cyber-physical systems; autonomous unmanned vehicles; robotics; ships and marine systems; process control; smart grids; offshore renewable energy; automated drilling; fisheries and aquaculture; biomedical technology; safety-critical systems; embedded and real-time systems; systems engineering; and instrumentation and sensor systems.

Work description

This PhD fellowship is tailored towards the interdisciplinary field Big Data Cybernetics. The main goal of this initiative is to combine temporal models from cybernetics with dimension-reduction techniques applying methods from multivariate data analysis.

The project concerns foremost modelling of financial data for forecasting, both quantitative and in terms of classification, such as fraud detection and unexpected trend developments.

Representative sampling is an important aspect of the data-driven modelling, together with proper statistical and cognitive model validation.

Methods for dimension reduction will be a central part of the modelling procedure to extract underlying factors in the time series analysis and make them interpretable and easy to validate statistically.

These subspace methods may be extended to multiblock models for cross-disciplinary linking of different types of input data.

Another possible topic is linking customer and product characteristics through return of investment or customer satisfaction with the so-called L-structure type of models (internal and external data sources): Linking a two-way data table of e.g. customer satisfaction (N customers x K services) to background info about both customers (N customers x J behavioral descriptor variables) AND services (M attributes x K services) via a compact two-way subspace regression model.

Qualifications

Candidates applying for this position should have an MSc degree in subjects which are relevant for high-dimensional time series modelling and a deep understanding of a variety of algorithms and methods. Examples of such subjects are cybernetics, applied mathematics, chemometrics and multivariate statistics. A strong background in applied linear algebra is expected. Knowledge about multivariate statistical data modelling methods and their validation is a requirement, both for quantitative prediction and classification. Some relevant methods are Principal Component Analysis, CART, multivariate regression with latent variables (PLSR etc) and other methods from statistics and chemometrics. Knowledge within Machine Learning methods, such as Support Vector Machines, Genetic Algorithms, ANN & deep learning etc, as well as in control theory, dynamic models in state-space and statistical time series analysis.

For the successful applicant, this represents an opportunity to play a central role in the development of a new interdisciplinary field. Programming skills in one or more frequently used languages for algorithmic development (Matlab, Python etc) is also a requirement. Teaching experience is an advantage. The applicant must be fluent in oral and written English and preferably Norwegian. Personal qualifications are also considered important.

Academic results, publications, relevant specialization, work or research experience, personal qualifications and motivation will be considered when evaluating the applicants.

Excellent English skills, written and spoken, are required. Applicants who do not master a Scandinavian language must provide evidence of good English language skills, written and spoken. The following tests can be used as such documentation: TOEFL, IELTS or Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). Minimum scores are:

  • TOEFL: 600 (paper-based test), 92 (Internet-based test)
  • IELTS: 6.5, with no section lower than 5.5 (only Academic IELTS test accepted)
  • CAE/CPE: grade B or A.

Formal regulations

Appointments are made in accordance with the regulations in force regarding terms of employment for PhD candidates issued by the Ministry of Education and Research, with relevant parts of the additional guidelines for appointment as a PhD candidate at NTNU.

Applicants must undertake to participate in an organized PhD programme of study during their period of employment. The person who is appointed must comply with the conditions that apply at any time to employees in the public sector. In addition, a contract will be signed regarding the period of employment.

The successful candidate will be appointed for a period of 3 years, with possible extension to a fourth year if the candidate and department agrees on teaching related duties.

Salary conditions

The position follows code 1017, salary grade 50 - 62 in the Norwegian State salary scale, gross NOK 436 500 - 537 300 per year, depending on qualifications. A deduction of 2% is made as a statutory contribution to the Norwegian Public Service Pension Fund.

General

NTNU can offer an informal and friendly workplace with dedicated colleagues, academic challenges and attractive schemes for home loans, insurance and pensions in the Norwegian Public Service Pension Fund.

The Faculty of Information Technology and Electrical Engineering wants to attract outstanding and creative candidates who can contribute to our ongoing research activities. We believe that diversity is important to achieve a good, inclusive working environment. We encourage all qualified candidates to apply, regardless of the gender, disability or cultural background.

The appointment is subject to the conditions in effect at any time for employees in the public sector, and assessments regarding the legislations regulating export control and knowledge transfer.

Under Section 25 of the Freedom of Information Act, information about the applicant may be made public even if the applicant has requested not to have his or her name entered on the list of applicants.

Further details about the position can be obtained from Adjunct Professor Frank Ove Westad, e-mail: frank.westad@ntnu.no .

Application requirements

Applications are to be submitted electronically through this page (www.jobbnorge.no). Preferably, all attachments should be combined into a single file.

The application must contain:

  • CV including information relevant for the qualifications and contact information for at least 2 reference persons
  • Certified copies of academic diplomas and transcripts
  • Applicants from universities outside of Norway are requested to send a diploma supplement (or a similar document) which describes in detail the study and grading system, and the rights for further studies associated with the obtained degree
  • A short research statement (max. 3 pages) including:
    • A short presentation of the motivation for a PhD study
    • Why the applicant is suited for the position
    • The applicant’s view of research challenges for the PhD position

Publications and any other work that the applicant wishes to be considered must also be enclosed. Joint works will be considered if a short summary outlining the applicant's contributions is attached.

Incomplete applications will not be considered.

Mark the application with the reference number: IE 128-2018.

Application deadline: 2018-08-15.

ABOUT THIS JOB

  • Deadline Wednesday, August 15, 2018
  • Employer NTNU - Norwegian University of Science and Technology
  • Website
  • Municipality Trondheim
  • Place of service Trondheim
  • Jobbnorge ID 154070
  • Internal ID 2018/18136
  • Scope Fulltime
  • Duration Project

ABOUT APPLICATIONS

  • Applications on this job are registered in an electronic form on jobbnorge.no
  • You must complete: Standard CV
  • Please refer to where you first saw this job advertised!

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