We are hiring two doctoral candidates, who will work on the Technical System of a larger
NWA-ORC project- ALIGN4energy, together with a broad interdisciplinary consortium of universities, and public and private organizations. The 4-year project ALIGN4Energy, funded by the Netherlands Science Foundation (NWO), aims to contribute to deploying clean energy technologies in residential homes rapidly and at scale. The ALIGN4energy consortium provides a total of 11 PhD positions aimed to simultaneously address the citizens, policy, and business perspectives of the residential energy transition. These two PhD positions offered by TU/e will be hosted at the Department of Built Environment, in the group of Information Systems in the Built Environment. These two PhDs will be working on work package 2 on the Technical System of the ALIGN4energ project.

Job Description

The energy and the climate crisis reinforced the urgency of energy efficiency in residential buildings in Europe. To meet its climate targets, the Netherlands needs to decarbonize the residential sector. This requires that a large number of distributed actors (households, owners associations, social housing associations, and citizen collectives) invest in clean energy technologies rapidly and at scale. Yet citizens' individual (homeowners, tenants) and collective (housing associations, homeowners' associations, etc) investments in energy-related home renovation will impact integrated energy systems, including correlations between energy grids, uncertainties and simultaneous peak loads.

On PhD project  will focus on agent-based simulation models to quantify individuals' decision-making under uncertainties and its emerging impacts on energy systems. The model will be used to simulate citizens' preferences, investment decisions and demand response engagement in the quantification of the simultaneous load factors of future-proof smart energy systems.

The second PhD project will focus on agent-based simulation models to quantify collective decision-making with multi-objective optimization. The model will be used to simulate the interactions among the decision-makers, in which the simultaneous load factors for each energy network infrastructure (especially thermal energy and power) determine the required network capacity and investment cost at all spatial scales from the individual building level to the (neighbourhood) network scale.

Both PhDs will take into consideration the institutional, market and behavioural influences and focus on answering the following questions: How can we effectively model the expected emerging behaviour as a result of individual and collective decision-making processes affected by energy efficiency investments? How can we estimate citizens' demand response potential taking into account behavioural, social and psychological dimensions leading to specific decisions on energy efficiency investments? How can we best model citizens' preferences and demand response engagement to estimate future electricity and heating consumption? Once the simulation models are built, the two PhDs will further explore the optimal solution for gas-free neighbourhoods and support both individual and collective clean energy investments. The PhD projects will use a wide range of public data sources and data collected in WP1 human systems, to develop and validate models and align with other WPs of the ALIGN4energy project, to integrate the human-energy system models and learning algorithms to support clean energy investment decisions.

We are looking for two candidates who meet the following requirements:

• A Master's degree in information management in the built environment, computer science, building science, or a related discipline with a strong data analytics focus;
• Affinity with and interest in sustainable transition in the built environment;
• Strong background and/or strong interest in agent-based modelling, simulation and optimization;
• Prior knowledge of sustainable renovation processes of housing and energy transition in the built environment;
• Excellent oral and written communication skills in English proven by a minimum score of
  95 in TOEFL or IELTS of 6.5 per sub-skill (writing, reading, listening, speaking);
• The ability to work in a team, take initiative, be result-oriented, organized, and creative; good people/communication skills;
• You are independent, self-motivated, eager to learn, and willing to work with partners to link real-world challenges to multidisciplinary research questions.

Furthermore, we consider it a plus if the candidate has prior knowledge of energy transition in the built environment and has gained a Master's degree in the relevant field with prior knowledge of energy transition and housing challenges in the built environment. In a nutshell, the candidate should have engineering and data analytics training, preferably have basic knowledge of programming and be willing to learn more, in particular agent-based models, to feel comfortable with the topic. The novel integrated system framework will be based on a transdisciplinary approach involving societal partners. Therefore, it is also important that the candidate will have good communication skills to work in a team and join the consortium meetings.

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.