PhD Scholarship in AI-Driven Optimization at DTU Management
You will join a supportive and dynamic research team that is working at the intersection of machine learning and operations research. Your main task will be to develop innovative methods for operator selection and solution acceptance in Adaptive Large Neighbourhood Search, guided by AI and machine learning techniques.
In collaboration with your supervisors and fellow researchers, you will contribute to the development of solvers for combinatorial optimization problems and apply them to real-world data from industrial partners.
We are a supportive team that will welcome you in a vibrant, interdisciplinary environment at one of Europe’s top technical universities – a university that values innovation, societal impact, and diversity. At DTU you will benefit from supportive policies for professional development, entrepreneurship, and work-life balance. We are located in Copenhagen, one of the world's most livable cities. The university offers an excellent quality of life, a safe and sustainable urban environment, and a vibrant cultural scene.
Research Area And Project Description
As part of your PhD, you will take advanced courses to build and deepen your skills, implement and evaluate algorithms, and develop your ability to write and present scientific work.
In the LeGO (Learning-Guided Optimization), we aim to develop next-generation heuristic and exact optimization methods enhanced by machine learning (ML). The overarching goal is to solve large-scale combinatorial optimization problems more efficiently, particularly in domains such as transportation planning and resource management, where better decision-making can benefit both society and the environment. In this PhD project, the primary focus will be on improving heuristic optimization methods, with a particular emphasis on Large Neighbourhood Search (LNS).
LNS is a powerful metaheuristic that constructs new solutions by repeatedly destroying and repairing parts of a current solution. The choice of which destroy and repair operators to apply, and when to accept new solutions, are central to the method’s performance—but they are also difficult to tune. In this project, we will explore how ML, including reinforcement learning and supervised learning, can be used to guide these choices intelligently and dynamically.
The first line of research will investigate how to learn effective operator selection strategies. This includes comparing classical methods such as Adaptive Large Neighbourhood Search (ALNS) with newer ML-based approaches and developing improved learning mechanisms that adapt online during the search. The second research line will focus on designing better acceptance criteria, deciding whether to accept a newly generated solution—potentially using deep reinforcement learning to move beyond traditional rules like simulated annealing.
These innovations will be tested within a structured software framework across a suite of practically relevant optimization problems in public transport planning and airport operations. The broader project also includes research on exact methods (column generation), and results from these methods will feed into the design of smarter LNS destroy/repair strategies, enabling further performance improvements.
The PhD student will join a strong academic team at DTU Management’s Management Science division. The project is led by Professors Stefan Ropke and Richard Lusby and involves international collaboration with leading researchers in machine learning and optimization. The work will involve collaborative research in algorithm design, software development, and empirical studies, leading to publishable contributions and shared scientific progress in the operations research community.
LeGO is funded by the Independent Research Fund Denmark (DFF). Two PhD students and several senior researchers will contribute to the project, along with industry partners who will support the application and validation of the developed methods.
Responsibilities and qualifications
As part of the LeGO project, you will help develop the next generation of tools for combinatorial optimization. We are looking for a candidate who is motivated by both technical curiosity and making a real-world impact. Ideally, you:
- Have experience with AI models (e.g., reinforcement learning, supervised learning, or graph neural networks)
- Are familiar with developing algorithms for combinatorial optimization problems
- Have knowledge about metaheuristics such as Large Neighbourhood Search
- Are motivated to contribute to a collaborative, interdisciplinary research environment
- Are eager to learn and explore new ideas at the intersection of ML and optimization
- Can program efficiently in one or more languages
- Communicate well in English, both orally and in writing
We value diverse perspectives and encourage applicants from all backgrounds to apply, even if you do not meet every single requirement.
You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree.
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 .
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 http://www.dtu.dk/english/about/job-and-career/working-at-dtu/career-paths.
Further information
Further information may be obtained from Professor Stefan Røpke (julie13ropkekevin49carllopez@dtu.dklam-hester.dkchurch-lambert.dk ) or Associate Professor Richard Lusby (qbriggsvictoriafernandezrmlu@cook.dkmadden.dkdtu.dkwatkins.dk).
You can read more about DTU Management at www.man.dtu.dk
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 no later than 5 September 2025 (23:59 Danish time) .
Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", 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 (in English) including official description of grading scale
- A research statement (2-3 pages) explaining your ideas and relevant literature.
You may apply prior to obtaining your master's degree but cannot begin before having 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 excellent research in the intersection between management, technology and economics. We develop solutions in close cooperation with companies and public authorities. Our research aims at strengthening welfare, productivity, and sustainability within society. A key element is the role of technology and its interaction with industry and individuals. The department is divided into four divisions: Technology & Business Studies, Management Science, Climate & Energy Policy and Transportation Science. The department offers a wide range of courses and programs at bachelor, master and PhD level across DTU’s study pro-grams. The department has around 200 employees, with around half coming from abroad.*
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.