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PhD Candidate in Machine Learning

Employer
NORWEGIAN UNIVERSITY OF SCIENCE & TECHNOLOGY - NTNU
Location
Trondheim, Norway
Closing date
31 Oct 2019

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Academic Discipline
Computer Science, Engineering & Technology
Job Type
Academic Posts, Postdocs
Contract Type
Temporary
Hours
Full Time

About the position

The Department of Computer Science has a vacancy for a PhD position within machine learning related to digital rocks

About the position

We have a vacancy for a PhD candidate at the Department of Computer Science at NTNU’s Trondheim campus. The work will be carried out in close collaboration with the company Pericore and will be affiliated with the Norwegian Open AI Lab. The candidate will perform research on next-generation machine learning methods related to digital rocks, in particular the development of new deep learning tools to process three-dimensional micro-tomographic images.

The PhD position is for three years.

The position reports to head of department

The human's future depends on our capacity to stop emitting CO2 to the atmosphere while keep growing to reduce poverty and improving the quality of life in developing countries. In this scenario, technologies such as Carbon dioxide Capture and Storage (CCS) and more efficient O&G production will play a very important role. The derivation of rock properties from high-resolution images (Digital Rocks) is a disruptive technology in that it can fundamentally alter how the industry measures the basic rock properties. This technology is based on the use of high-resolution 3D images to derive digital models of reservoir rocks.

Main duties and responsibilities

The human's future depends on our capacity to stop emitting CO2 to the atmosphere while keep growing to reduce poverty and improving the quality of life in developing countries. In this scenario, technologies such as Carbon dioxide Capture and Storage (CCS) and more efficient O&G production will play a very important role. The derivation of rock properties from high-resolution images (Digital Rocks) is a disruptive technology in that it can fundamentally alter how the industry measures the basic rock properties. This technology is based on the use of high-resolution 3D images to derive digital models of reservoir rocks.

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 [subject area] 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 position requires strong English oral and written skills (interacting with an international team, academic work, progress reports etc.)

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

Other qualifications:

  • Excellent written and oral English (excellent written and oral Norwegian or Scandinavian language skills are a plus but not a requirement).
  • Excellent programming skills and good knowledge of key programming languages and frameworks used in date science and machine learning.
  • Ability to work independently as well as collaboratively.

Applicants from non-English speaking countries outside EU/EEA/Switzerland must provide preliminary documentation of English language proficiency, in terms of an approved test. The following tests can be used: TOEFL, IELTS and Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English.

Further assessment of both written and oral English language skills and the ability to communicate fluently may be conducted in the continued selection process and during any interview for all applicants, including those providing the required documentation of English proficiency.

NTNU is committed to following evaluation criteria for research quality according to The San Francisco Declaration on Research Assessment - DORA.

Personal characteristics

We seek a candidate that is motivated, has good communication and networking skills, is proactive and forthcoming, and able to work independently if needed. The successful candidate should be creative and demonstrate persistence in addressing challenging research problems.

  • Strong analysis skills (e.g. abstract and mathematical thinking)
  • Strong writing skills (e.g. synthesizing and expressing complex ideas clearly)
  • Team player, collaborative, respectful and value the inputs and opinions of others
  • Self-motivated, ambitious, resourceful, result-oriented, and independent

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

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 3 years. Appointment to a PhD position requires admission to the PhD programme in Computer Science at NTNU in Trondheim, (see https://www.ntnu.edu/studies/phit). 

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, Trondheim offers great opportunities for education (including international schools) and possibilities to enjoy nature, culture and family life (http://trondheim.com/). Having a population of 200 000, Trondheim is a small city by international standards with low crime rates and little pollution. It also has easy access to a beautiful countryside with mountains and a dramatic coastline.

Questions about the position can be directed to Professor Frank Lindseth, email: frankl@ntnu.no or to the Head of department John Krogstie, email: john.krogstie@ntnu.no.

About the application:

The application must contain:

  • One-page cover letter including an explanation of how the candidate’s research interests and background would fit the position.
  • CV with information about education and relevant experience.
  • Copies of academic diplomas, transcripts, and certificates (Applicants from universities outside Norway are kindly requested to include a document describing in detail the study and grading system).
  • A short essay (up to 1000 words) describing the candidates’ view on current state-of-the-art methods and research challenges related to deep learning based segmentation of 3D images in general.
  • Any publications relevant to the research scope or any other work which the applicant wishes to be taken into account, e.g. their master thesis.
  • Names and contact information of at least three references.

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 your CV, diplomas and certificates. 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 2019/31686 when applying.

Application deadline: 31.10.2019

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 31st October 2019
Employer NTNU - Norwegian University of Science and Technology
Municipality Trondheim
Scope Fulltime
Duration Temporary
Place of service Trondheim

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