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The Machine Learning for Mobility group and the Operations Research group of the Technical University of Denmark (DTU), Department of Management Engineering, are looking for excellent applicants to pursue PhD studies, starting in October 2018.
The focus of this research lies in the intersection between Machine Learning and Operations Research: we want to work on Predictive Optimization.
Traditionally, prediction and optimization have been seen as two separate processes to be executed one after the other, assuming no interdependency between the two. This assumption, however, does not always hold. Take for example the transport industry. Assume an autonomous bus deployed on a dynamic network that changes depending on the incoming online requests of passengers. Given the route that the bus will take, the number of requests might also change. A potential passenger that can see the bus is near, might decide to make a request while the opposite can be true if the bus is far away hence longer waiting time are expected. Using the traditional approach, one could forecast the travel requests and then use an optimization technique to find an optimal route. The identified route might, however, change the passenger forecast as now more information is available. It would then be reasonable to include the route information into the forecast and re-calculate it. This is an example, where the forecast and the optimization are inter-dependable. Methods that combine forecasting and optimization are what we call Predictive Optimization.
In this project we aim at identifying one or more predictive optimization solution frameworks that can be applied to a range of problems. The project will be rooted around a real-life application, where data from a car-sharing company (e.g. Drive Now, GreenMobility), can be used (e.g. to plan the balancing of the fleet in Copenhagen). Other data sources and applications can be incorporated into the project to further test the developed frameworks.
This project is a partnership with the Technical University of Munich (TUM), particularly on the stochastic optimization component of the framework.
RESPONSIBILITIES AND TASKS
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- Literature review in the cross-road between prediction and optimisation, in order to generate a map of the current research and open problems;
- Design and implement one or more prediction optimization frameworks;
- Test the effectiveness of the frameworks on a case study based on data from a car-sharing company;
- Test the impact of using deterministic or stochastic optimisation within the framework/s.
QUALIFICATIONS
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- A Master's degree in computer science, management engineering, operations research, computer engineering, statistical physics, or related
- Excellent programming capabilities, in at least one 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 with a multi-national team
APPROVAL AND ENROLMENT
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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
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The assessment of the applicants will be made by 15 September 2018.
WE OFFER
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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
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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.
This project involves an extended stay in Singapore of 1 year, during those 3 years.
The workplace will be DTU Lyngby Campus, and includes a 6-month visit at TU Munich, in Germany.
FURTHER INFORMATION
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For more information, please contact Francisco C. Pereira, camara@dtu.dk, tel.: +45 4525 1496.
You can read more about DTU in www.dtu.dk.
APPLICATION
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Please submit your online application no later than 15 August 2018 (local time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link:
https://ssl1.peoplexs.com/Peoplexs22/CandidatesPortalNoLogin/ApplicationForm.cfm?PortalID=946&VacatureID=986984&CustomerCode=DTU
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)
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.
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 340 employees; including an academic staff of 190 and 68 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.
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