Robustness and Interpretable Machine/Deep Learning Analysis for High-Order Data: Theory, Algorithm and Application

About the position

We have a vacancy for a PhD fellowship at the Department of Computer Science Gjøvik (IDI).

This is a researcher training position aimed at providing promising researcher recruits the opportunity of academic development in the form of a doctoral degree. The candidate will be a member of the IDI Gjøvik group located in the NTNU Gjøvik campus.

Very recently, deep learning (DL) has been successfully applied in high-order data analytics for many domains including the visual and healthcare sector. However, high-profile researchers and practitioners (e.g., Gary Marcus in his “Deep Learning: A Critical Appraisal”) in the data community are raising critical concerns on the transparency of deep learning and neural networks (NN) in general:

The transparency issue, as yet unsolved, is a potential liability when using deep learning for problem domains like financial trades or medical diagnosis, in which human users might like to understand how a given system made a given decision. As Catherine O’Neill (2016) has pointed out, such opacity can also lead to serious issues of bias.

First, the traditional techniques have disclosed the high-order data sources well. However, it has been observed that state-of-the-art NN are highly vulnerable to (adversarial) perturbations. Tensor Learning and deep learning are powerful and versatile tools which can model a wide variety of multi-modal real-world data. Inherently, high-order tensor analysis poses a broad impact on several areas, as the tensor object in its own right accounts for own geometrical, statistical and computational popularity and adaptation.

Second, interpretability of machine/deep learning models has become crucial for (human) users to trust them. These trustworthy problems are the definite barriers for the adoption of these latest technologies into critical applications and justify urgent attention!

The position reports to the Head of the department

Main duties and responsibilities

•  Collaborate with other researchers, professionals, and acquire the transferrable knowledge and skills on processing and analyse the real- world data;
• Study and identify the NN-inspired tensor analysis for machine/deep learning models for the real-world data;
•  Explore different approaches and develop interpretable machine/deep learning methods with high trustworthiness

Qualification requirements

The PhD-position's main objective is to qualify for work in research positions. The qualification requirement is completion of a master’s degree or second degree (equivalent to 120 credits) with a strong academic background in computer science or equivalent education with a grade of B or better in terms of NTNU’s grading scale. Applicants with no letter grades from previous studies must have an equally good academic foundation. Applicants who are unable to meet these criteria may be considered only if they can document that they are particularly suitable candidates for education leading to a PhD degree.

The appointment is to be made in accordance with the regulations in force concerning State Employees and Civil Servants and national guidelines for appointment as PhD, postdoctor and research assistant

The candidate must have experience in image processing, pattern recognition, computer vision, natural language processing (NLP), machine learning, deep learning and AI. Strong programming skills and experiences with popular data processing and machine/deep learning frameworks are essential. The candidate must have strong mathematical, statistics and optimization background. The background in high-order tensor analysis for computer vision and NLP tasks is highly recommended. Understanding of multi-modal data, e.g., medical images, is also a plus. Candidates who have academic publications will be considered favourably.

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/writing 4.5 (paper-based test), 92/writing 22 (Internet-based test)
•  IELTS: 6.5, with no section lower than 5.5 (only Academic IELTS test accepted)
• CAE/CPE: grade B or A.

Candidates will be ranked according to qualifications described above, with emphasis on formal education, experience and personal suitability to the project.

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability, in terms of the qualification requirements specified in the advertisement. NTNU is committed to following evaluation criteria for research quality according to The San Francisco Declaration on Research Assessment - DORA.

Personal characteristics

• Team working and communicative
• Accountable
• Ethical

In the evaluation of which candidate is best qualified, emphasis will be placed on education, experience and personal suitability, as well as motivation, in terms of the qualification requirements specified in the advertisement

We offer

• exciting and stimulating tasks in a strong international academic environment
• an open and inclusive work environment with dedicated colleagues
• favourable terms in the Norwegian Public Service Pension Fund
• employee benefits

Salary and conditions

PhD candidates are remunerated in code 1017, and are normally remunerated at gross from NOK 479 600 before tax per year. From the salary, 2 % is deducted as a contribution to the Norwegian Public Service Pension Fund.

The period of employment is for 3 years or 4 years with 25% teaching duty work. Appointment to a PhD position requires admission to the PhD programme in Computer Science https://www.ntnu.edu/studies/phcos. 

As a PhD candidate, you undertake to participate in an organized PhD programme during the employment period. A condition of appointment is that you are in fact qualified for admission to the PhD programme within three months.

The engagement is to be made in accordance with the regulations in force concerning State Employees and Civil Servants, and the acts relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and attachment are seen to conflict with the criterias in the latter law will be prohibited from recruitment to NTNU. After the appointment you must assume that there may be changes in the area of work.

General information

A good work environment is characterized by diversity. We encourage qualified candidates to apply, regardless of their gender, functional capacity or cultural background. Under the Freedom of Information Act (offentleglova), information about the applicant may be made public even if the applicant has requested not to have their name entered on the list of applicants.

The national labour force must reflect the composition of the population to the greatest possible extent, NTNU wants to increase the proportion of women in its scientific posts. Women are encouraged to apply. Furthermore, Gjøvik offers good possibilities to enjoy nature and family life (https://en.visit-innlandet.no/) and is just a two hours’ train trip north of Oslo, the capitol of Norway. Having a population of 20 000, Gjøvik is a small city with low crime rates and little pollution.

Questions about the position can be directed to Assoc. Prof. Hao Wang at +47 70 16 15 34, hawa@ntnu.no   or the Head of the Department Prof. John Krogstie, email: john.krogstie@ntnu.no  

About the application:

Publications and other academic works that the applicant would like to be considered in the evaluation must accompany the application. Joint works will be considered. If it is difficult to identify the individual applicant's contribution to joint works, the applicant must include a brief description of his or her contribution.

Please submit your application electronically via jobbnorge.no with:

• Curriculum Vitae
• Diplomas and certificates
• Names and contact details of at least two reference persons.
• Research statement (max. 4 pages) including:
  • A short presentation of the motivation for a PhD study
  • How the applicant sees his/her background suitable
  • The applicant’s view of research challenges within the area of the PhD position
  • How the competence of the applicant can contribute to solving these challenges
  • A proposed research plan including appropriate research methods and what theories could be relevant to this field of research

Applications submitted elsewhere will not be considered. Diploma Supplement is required to attach for European Master Diplomas outside Norway. Chinese applicants are required to provide confirmation of Master Diploma from China Credentials Verification (CHSI): http://www.chsi.com.cn/en/).

Applicants invited for interview must include certified copies of transcripts and reference letters.

Please refer to the application number 2019/12976 when applying.

Application deadline: 01.02.2020

NTNU - knowledge for a better world

The Norwegian University of Science and Technology (NTNU) creates knowledge for a better world and solutions that can change everyday life.

Department of Computer Science 

We are the leading academic IT environment in Norway, and offer a wide range of theoretical and applied IT programmes of study at all levels. Our subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied data processing. The Department has groups in both Trondheim and Gjøvik. The Department of Computer Science is one of seven departments in the Faculty of Information Technology and Electrical Engineering .

Deadline 1st February 2020
Employer NTNU - Norwegian University of Science and Technology
Municipality Gjøvik Trondheim
Scope Fulltime
Duration Temporary
Place of service Gjøvik