Field service technicians at TDC NET drive approximately 80,000 kilometres every day, the equivalent of circling the Earth twice. The GREENFORCE project will improve the efficiency of field service by developing a software platform that will optimize demand management, remote resolution and routing. The platform will decrease kilometres driven by technicians, thus decreasing cost and CO2 emissions. GREENFORCE is a partnership between DTU Management, DTU Compute, Softwarehouse Qampo, and TDC NET, Denmark’s biggest supplier of digital infrastructure.
The project will use a combination of Artificial Intelligence (AI), Machine Learning (ML) and Operations Research (OR), which is denominated as Decision Science. The objectives are:
- Improved predictive maintenance by using ML to forecast component lifetime
- Enhanced remote resolution using AI to identify problems remotely and chat-bot utilization to support diagnostics and self-resolution, and
- Optimal route planning and task scheduling to reduce driving time and increase service level.
Responsibilities and qualifications
We are looking for ambitious candidates for two PhD projects (3-year, full-time). The two projects will focus on development of
- Demand Management. You will develop algorithms for situation dependent prediction of customer demand and estimation of prediction uncertainties. Various machine learning methods (including deep learning) will be assessed towards their applicability and advanced to tackle the domain specific challenges. Furthermore, you will develop suitable visualization tools for problem identification and diagnostics.
Starting date: 1st September 2021 (or according to mutual agreement).
- Remote Resolution. You will analyze large amounts of natural language data provided by TDC NET. Natural language processing (NLP) algorithms will be developed to classify customer demands remotely. Suitable Machine learning methods will be identified, utilized and advanced to tackle the domain specific tasks. Customer patterns of interest will be visualized adequately to be analyzed in collaboration with Stakeholders from TDC NET and Qampo.
Starting date: 1st January 2022 (or according to mutual agreement).
The two PhD positions will be hosted at DTU Compute, but interact closely with DTU Management, which is responsible for the Route Planning development of the project. As the project is aiming at commercialization of the developed solutions we will be using a prototyping approach with short sprints having well-defined goals. You will collaborate closely with decision scientists at Qampo and TDC NET, including a co-location with the partners in either Copenhagen or Århus, depending on your preference and suitability.
You should 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 in Machine Learning, Artificial Intelligence, Statistics or another closely related field.
In addition, you should have the following set of competencies.
- Experience in developing algorithms for ML, AI and Statistics.
- Experience in data mining and cleansing
- Good programming skills in R, Python and/or Matlab (experience with the Pytorch API will be considered a plus)
- Good project management skills
- Excellent collaboration skills
- Fluent in English, spoken as well as written
- Some basic knowledge of Operations Research and Route Planning would be good, to improve collaboration with other work packages
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.
The assessment of the applicants will be made by representatives from DTU Compute, Qampo and TDC NET.
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.
The position in Demand Management starts 1 September 2021 (or according to mutual agreement).
The position in Remote Resolution starts 1January 2022 (or according to mutual agreement).
You can read more about career paths at DTU here.
Further information may be obtained from Professor, Bjarne Ersbøll (DTU), tel.: +45 4525 3413, email: firstname.lastname@example.org, Associate Professor Andreas Baum, email: email@example.com, or Head of advanced analytics, Kristian Edlund (TDC NET), tel: +45 2091 6758.
You can read more about DTU Compute at www.compute.dtu.dk/english.
If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark.
Your complete online application must be submitted no later than 27 June 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) – Please state in your application which position you are applying for.
- 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)
- A research statement (2-3 pages) explaining your ideas and relevant literature
Applicants not providing all the above material will not be considered.
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.