PhD in Deep Reinforcement Learning for planning of lifespan-extension measures

PhD in Deep Reinforcement Learning for planning of lifespan-extension measures
PhD in Deep Reinforcement Learning for planning of lifespan-extension measures

Published	Deadline	Location
today	16 Oct	Eindhoven

Job description
Are you curious about making inner-city bridges and quay walls future-proof while keeping their cultural-historical value? Do you want to develop cutting-edge techniques from Deep Reinforcement Learning (DRL) for optimized planning of lifespan-extension measures for bridge and quay wall structures? Are you motivated to make these techniques applicable to specific use cases? We are looking for a PhD student with a focus on those topics.
Many old Dutch cities are characterized by their historic quay walls and bridges. In recent years however several walls have collapsed, and many more are in poor condition. The old structures have deteriorated due to deferred maintenance and changed social, technological, and environmental conditions.  Maintaining cultural heritage and, at the same time, preparing them for the future is a huge challenge. Lifespan-extension measures are a promising way to cope with this challenge instead of complete replacement. 
We are seeking a PhD candidate for the research project 'Sustainable Circular Life Extension Strategies for Inner-City Bridges and Quay Walls' (STABILITY). This exciting research is part of the Urbiquay Program of the Dutch National Research Agenda (NWA). It is funded by the Dutch Research Council (NWO) and in collaboration with University of Twente, Delft University of Technology, Saxion University of Applied Sciences, the Municipalities of Amsterdam, Den Hague, and Zwolle, and several contractors and engineering firms.
Deep Reinforcement Learning algorithms have demonstrated to be game-changers for complex and evolving problem settings having unstructured, diverse input data and uncertain system states. You, as a successful applicant, will develop an adaptive planning method for lifespan-extension measures based on deep reinforcement learning to account for the dynamics and uncertainties related to further deterioration of structures and the developments in the urban context. Your method will allow municipalities to consider the relevant social, technical, and environmental factors of the complex and uncertain urban environment for deciding on the type, moment, and location of the measures to be applied. The proposed approach should be able to execute multiple scenarios, thus rendering the optimal strategy for the renovation and replacement. With this, municipalities will be able to move from a reactive to a more proactive management approach.
Specifications

• max. 38 hours per week
• Eindhoven View on Google Maps

Eindhoven University of Technology (TU/e)

Requirements

• You have a Master's degree in Industrial Engineering, Operations Research, Computer Science, Data Science, Artificial Intelligence, or a similar field of study.
• You have a strong programming background, preferably in Python.
• You have a strong affinity with interdisciplinary research and enjoy collaborating with academics and practitioners.
• You can work on a challenging topic that has both fundamental and applied research aspects.
• Motivated to develop your teaching skills and coach students.
• Fluent in spoken and written English (C1 level).

Conditions of employment
A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P
  (min. €. 2.541 - max. € 3.247).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.

Additional information
About us
Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow. 
Curious to hear more about what it's like as a PhD candidate at TU/e? Please view the video.
Information
Do you recognize yourself in this profile and would you like to know more? Please contact Zaharah Bukhsh, z.bukhsh[a]tue.nl, or Yingqian Zhang, YQZhang[a]tue.nl.
Visit our website for more information about the application process or the conditions of employment.
You can also contact Susan Opgenoorth, HR Advisor, HRServices.IEIS[at]tue.nl or +31 40 2474465.
Are you inspired and would like to know more about working at TU/e?
Please visit our career page .
Application
We invite you to submit a complete application by using the apply button.
The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.
Please be aware that you can upload only 5 documents up to 2 MB each. If you have more than 5 documents you will have to combine them. Incomplete applications will not be considered.
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