The objective of this project is to develop theory, algorithms, and applications that can be used for smart building energy optimisation. The tasks involve investigation of the limitation of the current energy systems, the use of data-driven approaches for energy optimization, and the development of controlling systems. The PhD student will closely collaborate with industry partners of related projects, and other researchers; Develop models, algorithms, and methods
The ideal candidate should be holding a Master's degree in Engineering, Applied Mathematics, Computer Science or related fields with experience in data mining, machine and deep learning, and in particular reinforcement learning
PhD Scholarship in Reinforcement Learning for Autonomous Building Energy Management DTU Management Share on Facebook Share on Twitter Share on Linkedin Wednesday 04 Sep 19 Apply for this job Apply no later than 20 September 2019 Apply for the job at DTU Management by completing the following form. Apply online We invite highly motivated candidates to apply for the PhD position in Reinforcement Learning for Autonomous Building Energy Management. This is a 3-year scholarship funded by Nordic five Tech. Alliance. The PhD student will stay for two years in the Energy System Analysis Research Group at the Department of Management of the Technical University of Denmark (DTU) and for one year at the Department of Manufacturing and Civil Engineering of the Norwegian University of Science and Technology (NTNU). The student will be enrolled at both universities and can be awarded a double doctorate for successful defence. The energy consumption of buildings accounts for about 40% of the total global energy consumption. It is therefore essential to find innovative ways to reduce and optimise the energy. Today’s prevalence of digitasiation systems make it possible to use sensor technologies, communications and advanced control algorithms to optimise energy utilization, e.g., monitoring and controlling smart home devices to reduce energy consumption and costs. However, the main challenge is how to use the modern digital technologies to achieve intelligent energy management for autonomous buildings. Responsibilities and tasks The objective of this project is to develop theory, algorithms, and applications that can be used for smart building energy optimisation. The tasks involve investigation of the limitation of the current energy systems, the use of data-driven approaches for energy optimization, and the development of controlling systems. The PhD student will closely collaborate with industry partners of related projects, and other researchers. The responsibilities and tasks associated with the position include: Active participation in the research environments at DTU; Develop models, algorithms, and methods; Present research results at project meetings, and international conferences/workshops; Publi sh research results in peer-reviewed scientific journals/conference proceedings. Qualifications The ideal candidate should be holding a Master's degree in Engineering, Applied Mathematics, Computer Science or related fields with experience in data mining, machine and deep learning, and in particular reinforcement learning. The successful candidate has strong analytical and problem-solving skills, is proactive, self-motivated with critical thinking. Strong programming skills are required, preferably in Python or R. Fluent English speaking and strong paper writing skills are mandatory. You enjoy working in an international environment, solving practical problems, and collaborating with industrial project partners. 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 . 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 appointment terms 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. The place of work is Systems Analysis Group, Sustainability Division, Department of Technology, Management and Economics, Produktionstorvet 426, 2800 Kgs. Lyngby, Denmark. The academic approval of application is 3 October 2019 or as soon as possible thereafter. You can read more about career paths at DTU here . Further information Further information may be obtained from Senior Researcher Xiufeng Liu, email@example.com . You can read more about research at Systems Analysis Group at here . Application Please submit your online application no later than 20 September 2019 . 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. Sustainability is an internationally well-established division doing high-level research on energy and transport economics, climate change, urban systems, and sustainable development. We develop and apply methods from systems modeling, economics, econometrics and statistics, spatial analysis, and value chain analysis. The division has a staff of approximately 80 and employs researchers with competences in a range of related disciplines with a dominance of engineering and economics. Energy System Analysis is one among the four research groups in the division. It has around 20 staff and covers energy system modeling and big data analysis We emphasize statistical analysis, data management and Big Data alongside qualitative methods such as value chain analysis. 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 academic university collaborating globally with business, industry, government and public agencies.
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