PhD scholarship position in Artificial Intelligence for Policy Excellence i at Danmarks Tekniske Universitet

Kongens Lyngby, Capital Region of Denmark, Denmark -

Full Time

Start Date

Immediate

Expiry Date

12 May, 26

Salary

0.0

Posted On

11 Feb, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Policy Discovery, Multi-Agent RL, Uncertainty-Aware Decision-Making, Decision Support, Programming Skills, Climate Policy, Sustainability, Machine Learning, Applied Mathematics, Operations Research, Data Science, Simulation, Experimental Protocols, Ethics Constraints, Robustness Evaluation

Industry

Research Services

Description

We offer one PhD position in the Artificial Intelligence for Policy Excellence in the Climate Crisis (APEX) project at the Technical University of Denmark. The position focuses on reinforcement learning (RL) for policy discovery in a multi-sector “integrated modeling environment” that connects fast ML metamodels of simulators (e.g., transport, energy, environment, climate events). The aim is to develop RL methods that can search large policy spaces and support decision-makers in exploring robust strategies under deep uncertainty. Policy problems typically involve many control levers (e.g., taxes, incentives, investments) and uncertain exogenous drivers. Even with moderate dimensionality, the number of plausible scenarios becomes combinatorial, while full-fidelity simulators can take hours or days per run—making exhaustive analysis infeasible. APEX addresses this by coupling simulation metamodels (or surrogates) with RL, enabling systematic exploration of adaptive policies over time rather than one-shot scenario evaluation. The core research directions of the PhD project include: (i) multi-agent RL to jointly learn policies for multiple control parameters; (ii) uncertainty-aware decision-making; and (iii) decision-support to compare trade-offs and policy alternatives. The position is supervised by Professor Francisco Pereira, with co-supervision from colleagues in machine learning and transport systems, within the ELLIS network. The project is fully funded through the Novo Nordisk Foundation Data Science Distinguished Investigator programme. You will be based in the Intelligent Transport Systems (ITS) section of the Transport Division, Department of Technology, Management and Economics (DTU Management), at DTU. Responsibilities and qualifications Your primary tasks will be to: Become familiar with the literature on reinforcement learning for sequential decision-making under uncertainty, including multi-objective and constrained RL. Become familiar with the integrated modelling environment used in APEX (sectoral simulators and/or their ML metamodels), with support from the team. Formulate the policy-learning problem, including state/action representations, reward design (with consideration for fairness/ethics constraints where relevant), and evaluation metrics for robustness and tail risk. Create and curate datasets and experimental protocols (training/evaluation scenarios, baselines, ablations), including simulator- or metamodel-generated rollouts. Implement, test, and benchmark RL methods for policy discovery (e.g., multi-agent, multi-objective, uncertainty-aware, and/or safe RL), and document reproducible results. Co-supervise MSc students when relevant, participate in regular research meetings, and disseminate findings in peer-reviewed journals and top conferences. You must have a two-year Master’s degree (120 ECTS) in Computer Science, Machine Learning, Applied Mathematics, Operations Research, Engineering, or a closely related field, with strong programming skills and interest in decision-making for climate/sustainability policy (or related high-stakes domains). Candidates who have successfully completed the ELLIS PhD recruitment process will be strongly preferred. 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 DTU's rules for the PhD education. Assessment The assessment of the applicants will be made by 28 March 2026. 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. You can read more about career paths at DTU here. Further information Further information about the position may be obtained from Professor Francisco Pereira (camara@dtu.dk). You can read more about 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. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom seminar for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. Application procedure Your complete online application must be submitted by 3 March 2026 (23:59 Danish time). Applications must be submitted as a single PDF file containing all required materials to be considered. To apply, please click on the "Apply now" link, complete the online application form, and attach all your materials in English to a single PDF file. The file must include: A letter motivating the application (cover letter including 3 reference contacts) Curriculum vitae Grade transcripts and BSc/MSc diploma (in English), including an official description of the grading scale You may apply prior to obtaining your master's degree, but you cannot begin until you have received it. Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position. 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 behavior, regulation and policy analysis. The department offers teaching from introductory to advanced courses/projects at BSc, MSc, and PhD levels. The Department has a staff of approximately 350 people. The ITS section belongs to the Transport Science 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 behavior modelling, machine learning, and simulation. 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 mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.

Responsibilities

The primary tasks include familiarizing oneself with literature on reinforcement learning and formulating the policy-learning problem. The candidate will implement and benchmark RL methods for policy discovery and co-supervise MSc students.