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.

Responsibilities and tasks

Their  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, based in NTU

Thus, this PhD project will be about predictive machine learning modelling for Autonomous Bus real-time operations. More specifically, they 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.

Assessment

The assessment of the applicants will be made by 31 May 2017.

Offer

They 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.

Further information

For more information, please contact Francisco C. Pereira, tel.: +45 4525 14 96.

You can read more about DTU in www.dtu.dk.

Application

Please submit your online application no later than 10 May 2016. 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.