PhD Research Fellowship in Machine Learning for Autonomous, Adaptive Sensing in Internet of Things
Norwegian University of Science and Technology (NTNU) has a vacancy for a 100% position as a PhD research fellowship position affiliated with the Department of Information Security and Communication Technology (IIK) at Faculty of Information Technology and Electrical Engineering (IE)
Information about the department
The Department of Information Security and Communication Technology has 33 permanent scientific staff members, and in total approximately 80 employees. 50 persons work at NTNUs campus in Gjøvik and 30 at NTNUs campus in Trondheim. IIK carries out internationally competitive research organized in seven cross-disciplinary labs covering the academic areas of Internet of Things (IoT), Applied Cryptology, Intelligent Transport Systems, Quantifiable Performance and Dependability of Communication Systems, Biometry, Digital Forensics, and Information Security. The department provides teaching in BSc in IT Operations and Information Security, MSc programmes in Communication technology and Information Security, and PhD programmes in Telematics and Information Security. The department hosts and operates NTNUs Center for Cyber and Information Security, a public-private partnership with 25 members.
This is a researcher training position aimed at providing promising researcher recruits the opportunity of academic development in the form of a doctoral degree.
Part of the digitization in domains like smart cities, precision agriculture, manufacturing, and healthcare is the instrumentation of the physical world with IoT nodes to gather data, as well as make local changes to improve the domain’s overall performance. In current IoT solutions, decision-making is concentrated on the server side. The logic of IoT nodes is primitive and often static, with parameters set by developers at design-time. Since IoT devices are deployed in environments with changing conditions, such statically chosen parameters are suboptimal. Individual, manual control and adaptation, however, is not possible due to the scale of the systems and the complexity of factors to take into account. As a result, IoT nodes fail during operation, deliver much less data than desirable, or need to be equipped with far larger energy sources than ideally necessary. Instead, future IoT devices must act intelligently and independently to serve their application goals optimally given their resource situation.
Within this PhD project, we seek to evolve methods to control the behavior of IoT nodes that are subject to energy-constraints and intermittent communications based on statistical machine learning methods. We aim to make IoT systems to be more intelligent with respect to sensing strategies, resource management and overall orchestration. This entails more advanced algorithms and optimised strategies and the use of learning techniques, so that IoT systems can autonomously adapt to changing environments and application requirements. This evolution should serve two purposes, (1) to improve the utility for specific domains and (2) to optimise the autonomous operation of the IoT system itself. This requires a consideration of the system across established network layering, i.e., from electronics to application, and its critical components, such as the electronics and energy source of the nodes, data fusion algorithms in the data center, and overall system and communication architectures that are aware that sensor nodes autonomously adapt to their environment.
The research will be carried out in an interdisciplinary environment of several research groups and labs. The cross-department research group around Autonomous, Adaptive Sensing (IES, IMF, IIK) is host for the ART project and is responsible for the main supervision of the project. The Telenor-NTNU AI Lab has expertise within machine learning, decision support systems and reinforcement learning and provides a thriving environment for AI research. The NTNU Internet of Things Lab has experimentation test beds for autonomous sensors and data that can be used as starting point for simulations and realistic test beds.
The appointment is for a term of 3 years without teaching assistance, or for 4 years including 25% of teaching assistance.
Due to the cross-disciplinary nature of the proposed project, the candidate should have a background in either communication technology or machine learning. Ideally, the candidate has experience in both of them, or alternatively shows the capability to cover both areas and apply machine learning, statistics and applied mathematics in an engineering setting. We therefore seek a highly-motivated individual holding a master’s degree in communication- and information technology, computer science, electrical engineering, statistical machine learning, applied mathematics or equivalent. The average grade must be B or better.
Further, the applicants must have experience with programming. Experience with cross-disciplinary research is an advantage. Experience with embedded systems and electronics count as positive, but are not strictly required. Publication activities in the aforementioned disciplines will be considered an advantage. Motivation for fundamental scientific research of practical relevance is essential. The applicant should have good communication skills and be capable to collaborate with other researchers in the cross-disciplinary group of people connected to this project.
See here for regulations concerning appointment and promotion to teaching and research posts: https://www.regjeringen.no/no/dokumenter/regulations- concerning-appointment-and-promotion-to-teaching-and-research-posts/id2519258/
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.
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.
Applicants must be qualified for admission as PhD students at NTNU. See http://www.ime.ntnu.no/forskning/phd for information about PhD studies at NTNU. Together with the application, include a description of the research work that is planned for completion during the period of the grant.
The position follows code 1017 Research fellow, salary grade 51-62 in the Norwegian State salary scale, gross 449.400 - 537.700 per year, depending on qualifications. A deduction of 2% is made as a statutory contribution to the Norwegian Public Service Pension Fund.
We can offer an informal and friendly workplace with dedicated colleagues academic challenges, attractive schemes for housing loan, insurance and pension 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.
For further information about the position, please contact Associate Professor Frank Alexander Kraemer, firstname.lastname@example.org. The appointment is subject to the conditions in effect at any time for employees in the public sector.
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.
The application must be sent electronically through this page (www.jobbnorge.no) with information about education and relevant experience (all in one combined PDF file). Mark the application with the IE code given below.
The application must contain information of educational background and prior training, exams, and work experience. In addition, the applicant must submit a research statement (max. 3 pages), detailing research interests and initial plans with regard to the above project description. The statement should also describe why the applicant is suited for the position, and how the project relates to previous education, research and competence. Publications and other work that the applicant wishes to be taken into account must be enclosed (including a brief description of the contribution if not obvious).
The application must contain:
- Curriculum vitae (CV) with information about the candidate’s prior training, exams, and work experience
- Certified copies of transcripts and diplomas
- Applicants from universities outside Norway are kindly requested to send a diploma supplement or a similar describes in detail the study and grading system and the rights for further studies associated with the obtained degree
- Research statement (max. 3 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 AI or machine learning methods can address challenges within IoT
- How the competence of the applicant can contribute to solving these challenges
- Names and contact information of at least 2 reference persons
- A copy of the master thesis (in PDF), or, for those who are near to completion of their MSc, an extended abstract combined with a statement of how and when the applicant plans to complete the thesis.
Incomplete applications will not be considered.
For further information about the application process, please contact Signe J. Talukder, email@example.com.
The application deadline is: August 12, 2018
Mark the application: IE-140-2018
About this job
- Deadline Sunday, August 12, 2018
- Employer NTNU - Norwegian University of Science and Technology
- Municipality Trondheim
- Place of service Trondheim
- Jobbnorge ID 155000
- Internal ID 2018/20294
- Scope Fulltime
- Duration Temporary
- 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!