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Apply no later than 10 May 2017
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The Machine Learning for Mobility group (MLM) of the Technical University of Denmark (DTU), Department of Management Engineering, is looking for excellent applicants to pursue PhD studies, starting in August 2017.
We do methodological research in Statistics and Machine Learning, with particular focus on Transportation problems. Given the complexity of our cities and transportation systems, we believe we need both a strong methodological background as well as deep domain knowledge to have true impact in the real-world. Thus, we always aim to contribute to both research communities, of Machine Learning, and Transportation. It is an ambitious, yet very exciting place to be!
Responsibilities and tasks
Our group, together with the Dauwels lab and the Energy Research Institute @NTU (Eri@n), from the Nanyang Technological University (NTU), are working on a concept of demand responsive on-campus shuttle service, where an autonomous bus carries passengers between different points of a University campus, depending on anticipated demand. In this project, two components work closely together:
- Demand prediction - the subject of this PhD, based in DTU
- System optimisation - the subject of another PhD, base in NTU
Thus, this PhD project will be about predictive machine learning modelling for Autonomous Bus real-time operations. More specifically, we will work with demand predictions for the near future (how many people are going from A to B in the next 5, 15, 30 minutes…). Only by properly anticipating such trips it will be possible to optimize resources to meet the needs of the city.
Plenty of existing approaches for demand prediction tend to rely on “regular” behaviour (e.g. commuting trips) and large amounts of data, and the results are known to be good. However, a University campus is a very dynamic place, and the algorithm has to be able to deal with many exceptions (e.g. exam seasons, holidays, special events, harsh weather). Current algorithms tend to be fragile to such scenarios, first due to insufficient relevant data, and also due to having been designed and trained with data mostly related to “regular” behaviour.
The objective is thus to advance in current and future paradigms for more resilient Machine Learning methods. The PhD is not restricted to a particular “flavour” of Machine Learning (it can be with Deep Learning, Probabilistic Graphical Models, Gaussian Processes, or others), although reasonable proficiency in the Bayesian framework is important. Qualifications
- A Master’s degree in computer science, computer engineering, statistical physics, transportation engineering, or related
- Excellent programming capabilities, in any scientific language (e.g. Python, Matlab, R, Julia)
- Excellent background in statistics and probabilities
The following soft skills are also important:
- Curiosity and interest about current and future mobility challenges (e.g. autonomous mobility, traffic prediction, travel behaviour)
- Good communication skills in English, both written and orally
- Willingness to engage in group-work a multi-national team
Approval and Enrolment
The scholarships for the PhD degree are subject to academic approval, and the candidates will be enrolled in one of the general degree programmes of DTU. For information about the general requirements for enrolment and the general planning of the scholarship studies, please see the DTU PhD Guide.
The assessment of the applicants will be made by 31 May 2017.
We offer an interesting and challenging job in an international environment focusing on education, research, scientific advice and innovation, which contribute to enhancing the economy and improving social welfare. We strive for academic excellence, collegial respect and freedom tempered by responsibility. The Technical University of Denmark (DTU) is a leading technical university in northern Europe and benchmarks with the best universities in the world.
Salary and appointment terms
The salary and appointment terms are consistent with the current rules for PhD degree students. The period of employment is 3 years. This project involves an extended stay in Singapore of 1 year, during those 3 years.
The workplace will be DTU Lyngby Campus, with a 1 year visit to the NTU campus, in Singapore.
For more information, please contact Francisco C. Pereira, tel.: +45 4525 14 96.
You can read more about DTU in www.dtu.dk.
Please submit your online application no later than 10 May 2017. Applications must be submitted as one pdf file containing all materials to be given consideration. To apply, please open the link "Apply online," fill in 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)
Candidates may apply prior to obtaining their 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.
The Machine Learning for Mobility group belongs to the Transport Modeling division of the department of Management Engineering at DTU. The division conducts research and teaching in the field of traffic and transport planning, with particular focus on behavior modeling, machine learning and simulation.
DTU Management Engineering contributes actively to the development of management tools and optimization of processes by using and re-thinking theoretical engineering perspectives, models, and methods. Through our research and teaching, we ensure an innovative, competitive, and sustainable organization and use of technologies within areas such as energy and climate, transportation, production and health, both domestic and abroad. DTU Management Engineering has 350 employees, including around 70 PhD students. More than 20% of our employees are from abroad and a total of 38 different nationalities are represented at the Department.
DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 5,800 advance science and technology to create innovative solutions that meet the demands of society; and our 10,600 students are being educated to address the technological challenges of the future. DTU is an independent academic university collaborating globally with business, industry, government, and public agencies.