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Position: PhD Student in Automatic Specification in Dynamic Behavior Modeling and Specification through Advanced Statistics and Machine Learning Methods
Institution: Technical University of Denmark
Location: Kongens Lyngby, Lyngby‐Taarbæk Municipality, Denmark
Duties: The focus of this research thus lies in the intersection between Artificial Intelligence and Choice Modelling. The Machine Learning for Smart Mobility Group and the Transport Modelling Division of the Department of Management Engineering at the Technical University of Denmark (DTU) is looking for excellent applicants to join the Division, starting on August 1st, 2019 or later
Requirements: A MSc degree in Transportation Modelling, Computer Science, Applied Mathematics and Statistics, Cognitive Sciences, Behaviour Studies, Urban Planning or related is required; Excellent programming capabilities, in at least one scientific language (e.g. Python, Matlab, R, Julia) is required; Excellent background in statistics and probabilities is required; Transportation Modelling disciplines in the education background are preferable; The following soft skills are also important: Curiosity and interest about current and future mobility challenges (e.g. smart and integrated mobility and travel behaviour); Good communication skills in English, both written and orally; Willingness to engage in group-work with a multi-national team
   
Text: PhD Student in Automatic Specification in Dynamic Behavior Modeling and Specification through Advanced Statistics and Machine Learning Methods DTU Management Share on Facebook Share on Twitter Share on Linkedin Monday 03 Jun 19 Apply for this job Apply no later than 24 June 2019 Apply for the job at DTU Management by completing the following form. Apply online A central task in demand modeling, and econometrics in general, is to fully specify utility functions, that typically participate in choice models. These are essential in areas such as transport, economics, marketing, communications, energy systems and many others. It is such functions that model how people make choices, and are a known source of important information such as value of time , pricingelasticities , willingness to pay , and so on. With the arrival of Big Data, the challenge has become how to extend current knowledge, to build better models. Furthermore, traditional methods find it hard to deal with the dynamic nature of behavior (we make different choices in daily lives, depending on the context, and even change our preferences throughtout our lives). From a machine learning perspective, these challenge are generally known to both feature selection , representation and online learning . Following earlier work from the same team, on automatic specification at a first order level (i.e. selecting, among a given set of variables, which ones provide the best model, for a static model), we now turn ourselves to more advanced machine learning and statistical methods , to approach higher order and dynamic levels. In other words, models with latent state variables, automatic segmentation, non-linear transformations, embeddings, or non-parametric factors. The focus of this research thus lies in the intersection between Artificial Intelligence and Choice Modelling. The Machine Learning for Smart Mobility Group and the Transport Modelling Division of the Department of Management Engineering at the Technical University of Denmark (DTU) is looking for excellent applicants to join the Division, starting on August 1 st , 2019 or later. We are looking for excellent applicants with MSc background on Transportation, Computer Science, Applied Mathematics and Statistics, Cognitive Sciences, Behaviour Studies, Urban Planning or, and with the interest and ambition to pursue PhD studies. This is a collaboration project between DTU, École Polytechnique Federale de Lausanne (EPFL, Switzerland), and the Israel Institute of Technology (Technion, Israel). The students involved are expected to do extended visits to all three institutions. Project Overview In this project you will focus on the development and understanding of the dynamics in individual decision making related to transportation, and design new methodologies for their specification from data and models. Both choice modelling classical methods and emerging machine learning techniques will be combined with stochastic process formulations to model such dynamics, especially focusing on scenarios with the introduction of a new smart mobility service in a given urban area. Different individual specific data sources, from paper to smartphone based travel surveys will be used in this project. Responsibilities and tasks Literature review in dynamic behavioural processes and automatic specification, in order to generate a map of the current research and open problems; Design and estimate alternative formulations for selected dynamic decision making processes using an individual travel data set - using Classical Discrete Choice, Deep Learning, Causal Discovery and Probabilistic Graphical Model paradigms; Test the effectiveness of the frameworks based using simulation, and combine with Optimization and other automatic specification methods (with EPFL and Technion); The student is expected to spend 6-12 months in EPFL and Technion (at least 3 months in each). Qualifications A MSc degree in Transportation Modelling, Computer Science, Applied Mathematics and Statistics, Cognitive Sciences, Behaviour Studies, Urban Planning or related is required; Excellent programming capabilities, in at least one scientific language (e.g. Python, Matlab, R, Julia) is required; Excellent background in statistics and probabilities is required; Transportation Modelling disciplines in the education background are preferable; The following soft skills are also important: Curiosity and interest about current and future mobility challenges (e.g. smart and integrated mobility and 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 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 15 July 2019 . 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, camara@dtu.dk . You can read more about DTU Management Engineering in www.man.dtu.dk/english . Application Please submit your online application no later than 24 June 2019 (local 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. Applications and enclosures received after the deadline will not be considered. 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 Modelling 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 Engineering 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 introductionary to advanced courses/projects at BSc, MSc and PhD level. The Department has a staff of app. 350. Read more here . DTU is a technical university providing internationally leading research, education, innovation and scientific advice. Our staff of 6,000 advance science and technology to create innovative solutions that meet the demands of society, and our 11,200 students are being educated to address the technological challenges of the future. DTU is an independent university collaborating globally with business, industry, government and public agencies.
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