PhD scholarship in Intelligent Maintenance of IoT Infrastructures

Updated: about 5 hours ago

Are you interested in conducting cutting edge research in the intersection of the Internet of Things (IoT) and Artificial Intelligence (AI) focusing on resource-limited edge IoT devices? Are you a hands-on person, interested in low-level programming, applied machine learning, and deploying IoT devices in the real world? The Embedded Systems Engineering (ESE) section of DTU Compute offers a PhD scholarship funded by the newly established Digital Research Centre Denmark (DIREC) . The position is available from June 1, 2021, or later according to mutual agreement.

With the emergence of IoT and the Industry 4.0 revolution, multiple industries automate and enhance the efficiency of their operations using networks of embedded devices, such as industrial sensors, actuators, and robots. Such IoT deployments are expected to operate robustly for extended periods of time. To that end, upon the deployment of an IoT network, maintenance operations need to be scheduled and performed. Examples of maintenance operations include recharging batteries and replacing faulty components, amongst others. IoT deployments often support critical operations that must not be interrupted. Hence, the scheduling of maintenance operations must also take into account application-layer constraints (for example, when some downtime is acceptable without risking delays in fulfilling customer contracts). How can we leverage machine learning and AI to manage and maintain IoT infrastructures intelligently and efficiently?

Responsibilities
ThisPhD project is focused on developing novel algorithms and systems to monitor, plan and support the maintenance of IoT deployments in real-time. To that end, the project will investigate and propose novel real-time AI-based predictive maintenance algorithms. Real-time predictions will be used as input to a maintenance planning framework that generates optimised maintenance schedules.

Some tentative tasks within the project include:

  • Resource-efficient maintenance planning in a resource-constrained industrial IoT environment based on on-device embedded machine learning.
  • Development of AI-based predictive maintenance algorithms based on data available from the project’s partners or other public datasets.
  • Investigation of on-device predictive maintenance in severely resource-constrained platforms, such as industrial sensors that are based on tiny microcontrollers.
  • Real-time maintenance planning in an unconstrained industrial IoT environment based on a traditional centralised learning infrastructure. The trade-offs between on-device and centralised machine learning shall be investigated.
  • Deployment and monitoring of a small IoT testbed at DTU Compute for the experimental evaluation of the proposed intelligent maintenance framework.

Qualifications
You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Applicants with a 300 ECTS (i.e., 5 years) education at bachelor and master level are also encouraged to apply.

You must have a master’s degree in computer science or computer engineering or equivalent. You must have a strong background inapplied machine learning and low-level systems programming in C. Knowledge of constrained programming is desirable.Some experience with low-power wireless networking protocols is also desirable. Experience in writing and publishing scientific papers is an advantage. You must be fluent in English, both speaking and writing, and possess excellent communication skills.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide .

Assessment
The assessment of the applicants will be made by Associate Professor Xenofon Fafoutis, DTU Compute, and Professor Jan Madsen, DTU Compute.

We offer
DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

Salary and appointment terms
The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years.

You can read more about career paths at DTU here .  

Further information
Further information may be obtained from Associate Professor Xenofon Fafoutis (xefa@dtu.dk ).

You can read more about DTU Compute at www.compute.dtu.dk/english . You can read more about the ESE section of DTU Compute at www.compute.dtu.dk/english/research/research-sections/ese . You can read more about DIREC at https://direc.dk/ . 

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark

Application procedure
Your complete online application must be submitted no later than 15 May 2021 (Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply online", fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

  • A letter motivating the application (cover letter)
  • Curriculum vitae
  • Grade transcripts and BSc/MSc diploma
  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here )

You may apply prior to ob­tai­ning your master's degree but cannot begin before having received it.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

DTU Compute
DTU Compute is a unique and internationally recognized academic environment spanning the science disciplines mathematics, statistics, computer science, and engineering. We conduct research, teaching and innovation of high international standard - producing new knowledge and technology-based solutions to societal challenges. We have a long-term involvement in applied and interdisciplinary research, big data and data science, artificial intelligence (AI), internet of things (IoT), smart and secure societies, smart manufacturing, and life science.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear vision to develop and create value using science and engineering to benefit society. That vision lives on today. DTU has 12,000 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. Our main campus is in Kgs. Lyngby north of Copenhagen and we have campuses in Roskilde and Ballerup and in Sisimiut in Greenland.


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