The focus of this research is the following question: What is the best way to expand an EV charging network, such that we both minimize cost and maximize usage? This is first and foremost an Optimization problem, more specifically the Electric Vehicle Charging Station Location (EVCSL) problem within the Operations Research (OR) field. In our case, this component is directly related with another one (corresponding to another PhD student) on EV charging Demand prediction using Machine Learning methods
A MSc degree in Operations Research, Computer Science, Applied Mathematics and Statistics or related is required; Excellent programming capabilities in at least one scientific language (preferably Python, but others are acceptable) is required; Excellent background in statistics and optimization is required; Transportation Modelling or Energy Systems disciplines in the education background is also favoured
PhD Scholarship in Charging Network optimization using Machine Learning and Operations Research DTU Management Share on Facebook Share on Twitter Share on Linkedin Tuesday 12 Jan 21 Apply for this job Apply no later than 31 January 2021 Apply for the job at DTU Management by completing the following form. Apply online Outside of ownership costs and battery range, the biggest perceived barrier for Electrical Vehicle (EV) adoption among consumers is the need for charging infrastructure. Hence, for charging infrastructure service providers, strategic selection of new station locations is essential not only for gaining a competitive edge but also for growing the EV market. Particularly in urban areas, EV adoption will not be significant without an adequate public charging infrastructure. No policy that favours EV ownership or utilization is realistic without the support of a good level of charging supply that makes citizens comfortable enough for their daily routines. However, charging service providers, such as e.On and Clever in Denmark, struggle with the hard but unavoidable question of where to install new charging stations such that usage is maximized while costs are minimized. Not less important is how to do it in an incremental way, supporting incremental EV adoption as it grows throughout the network, reaching new areas, and appealing to new potential EV users. The focus of this research is the following question: What is the best way to expand an EV charging network, such that we both minimize cost and maximize usage? This is first and foremost an Optimization problem, more specifically the Electric Vehicle Charging Station Location (EVCSL) problem within the Operations Research (OR) field. In our case, this component is directly related with another one (corresponding to another PhD student) on EV charging Demand prediction using Machine Learning methods. This is thus a collaborative project where we explore the interactions between Machine Learning and Operations Research, where the responsibility of the student is to explore methods that address the EVCSL constrained optimization problem from an OR ML perspective. The Machine Learning for Smart Mobility group (MLSM) of the Transport Division of the Department of Management at the Technical University of Denmark (DTU) is looking for excellent applicants to join the Division, starting on March 1, 2021. This PhD is funded by the DFF project “Behavior Oracle for always-ON electrical mobility (BOON)”, which aims to support the electrical mobility transition in Denmark, towards sustainable transport. We are looking for excellent applicants with MSc background on Computer Science, Operations Research, Applied Mathematics, Statistics or related, and with the interest and ambition to pursue PhD studies. BOON is a also collaboration between DTU, Cornell Universtiy and Massachusetts Institute of Technology (MIT), and this PhD will be co-supervised by Samitha Samaranayke (Cornell University, NY) and thus will include a 6 months stay in Ithaca, NY. Qualifications A MSc degree in Operations Research, Computer Science, Applied Mathematics and Statistics or related is required; Excellent programming capabilities in at least one scientific language (preferably Python, but others are acceptable) is required; Excellent background in statistics and optimization is required; Transportation Modelling or Energy Systems disciplines in the education background is also favoured; The following soft skills are also important: Curiosity and interest about current and future mobility challenges (e.g. green transition, electrical mobility, behavior modeling); Good communication skills in English, both written and orally Willingness to engage in group-work with a multi-national team; 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 10 February 2021. 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 terms of employment 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 For more information, please contact Francisco C. Pereira, firstname.lastname@example.org or Inon Peled email@example.com . You can read more about the Machine Learning for Smart Mobility group at http://mlsm.man.dtu.dk/ and DTU Management at www.man.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 . Application Please submit your online application no later than 31 January 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 ) 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 Smart Mobility group belongs to the Transport division of the Department of Technology, Management and Economics (DTU Management) at DTU. The division conducts research and teaching in the field of traffic and transport planning, with particular focus on behaviour modelling, machine learning and simulation. DTU Management conducts high-level research and teaching with a focus on sustainability, transport, innovation and management science. Our goal is to create knowledge on the societal aspects of technology - including the interaction between technology and sustainability, business growth, infrastructure and prosperity. Therefore, we explore and create value in the areas of management science, innovation and design thinking, business analytics, systems and risk analyses, human behaviour, regulation and policy analysis. The department offers teaching from introductory to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of app. 350. Read more here . 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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