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PhD scholarship in Human Navigation, Artificial Intelligence and Neuroscience for Human-Centric Urban Mobility - DTU Management

Danmarks Tekniske Universitet (DTU)

DTU Management invites applications for a 3-year PhD scholarship associated with the project “Brains in Motion: Explaining How Humans Learn and Adapt to Navigate Urban Environments Using Artificial Intelligence and Neuroscience.”

The PhD project will be carried out at the Department of Technology, Management and Economics (DTU Management) at the Technical University of Denmark (DTU), in close collaboration with Imperial College London and the new DTU Smart Sensing Lab. The project is supervised by Associate Professor Rico Krueger and co-supervised by Associate Professor Carlos L. Azevedo at DTU, with Professor Aldo Faisal from Imperial College London involved as international project partner.

Background
Navigating modern cities is a complex and cognitively demanding task. People continuously learn, adapt and update their behaviours as they encounter new places, changing infrastructure, disruptions, new technologies and evolving mobility systems. Yet, current models of navigation and route choice often assume stable preferences and near-perfect knowledge of the environment. As a result, they provide limited insights into how people learn to move through cities over time.

This PhD project will develop a new generation of biologically grounded behavioural models that explicitly account for learning and adaptation during navigation in urban environments. The project aims to connect observed mobility behaviour with underlying cognitive and neurophysiological mechanisms, thereby supporting the design of more intuitive, inclusive and human-centric cities and transport systems.

Research objectives and tasks
You will contribute to the development and empirical validation of models that explain how humans learn, adapt and stabilise navigation behaviour in urban environments. The project will combine methods from transportation science, artificial intelligence, computational neuroscience and behavioural experimentation.

Key research tasks include:

  • developing learning-based behavioural models of navigation, route choice and adaptation;
  • applying reinforcement learning, probabilistic modelling and computational neuroscience to human mobility behaviour;
  • modelling how people build and update internal representations of urban space;
  • integrating behavioural data with neurophysiological markers such as eye movements, cognitive load, attention dynamics and EEG-based measures;
  • designing and conducting immersive virtual reality experiments and naturalistic field studies;
  • studying behavioural adaptation to changes such as route closures, infrastructure modifications and new mobility technologies;
  • translating model insights into tools and knowledge relevant for urban planning and transport system design.

The project will have a strong empirical component. The PhD student will work with behavioural data, immersive experimental environments, and human-centric sensing technologies such as eye-tracking, wearables and EEG.

International collaboration
The PhD project is part of a new strategic collaboration between DTU and Imperial College London. You will undertake a 6-month research stay at Professor Aldo Faisal’s Brain & Behaviour Lab at Imperial College London. During the stay, you will acquire technical and methodological expertise in measuring and analysing neurophysiological processes using state-of-the-art sensing equipment. The collaboration will also include joint supervision, regular project meetings, co-authored scientific publications and interaction with a complementary PhD project at Imperial College London.

Qualifications
We are looking for a highly motivated candidate with a strong interest in human behaviour, urban mobility, artificial intelligence and/or neuroscience.

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. Your degree should be in a relevant field, such as:

  • cognitive science, computational neuroscience, computational psychology or behavioural science;
  • transport modelling, transportation science or urban mobility;
  • data science, artificial intelligence, machine learning or computer science;
  • human-computer interaction, spatial cognition or related areas;
  • engineering, applied mathematics, statistics or another quantitatively oriented discipline.

The ideal candidate will have several of the following qualifications:

  • strong quantitative and programming skills;
  • experience with Python, R, Julia, MATLAB or similar tools;
  • knowledge of behavioural modelling, reinforcement learning, probabilistic modelling, machine learning or statistical inference;
  • interest in experimental design and human-subject studies;
  • experience with eye-tracking, EEG, physiological sensing, VR experiments or field experiments, or a strong motivation to learn these methods;
  • excellent written and spoken English;
  • be well-versed in the productive and responsible use of AI technologies;
  • ability to work independently as well as in an interdisciplinary and international research team.

Prior experience with transport modelling, cognitive neuroscience or empirical behavioural research and modelling is an advantage, but we do not expect you to be experts in all areas. The project is interdisciplinary, and you will be supported in developing the necessary methodological competences.

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.

The starting date is 1 January 2027 (or later according to mutual agreement). The position is a full-time position.

You can read more about career paths at DTU here .

Further information
Further information about the position may be obtained from Associate Professor Rico Krueger ( Se e-mailadresse på studerendeonline.dk ).

You can read more about DTU Management at .

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 15 September 2026 (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 concise letter motivating the application (cover letter)
  • Curriculum vitae
  • Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale
  • Names and contact details of at least two referees

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.

About the department
The Intelligent Transportation Systems (ITS) section belongs to the Division for Transportation Science 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 behavioural modelling, machine learning and simulation.

DTU Management conducts excellent research at the intersection of management, technology and economics. We develop solutions in close cooperation with companies and public authorities. Our research aims at strengthening welfare, productivity and sustainability. A key element is the role of technology and its interaction with industry and individuals. The department offers a wide range of courses and programs at bachelor, master, and PhD levels across DTU's study programs. The department has 250+ employees with around half of staff coming from abroad.

DTU – For the benefit of society since 1829
DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.

Location
Kgs. Lyngby, Denmark

Apply Before
2026-09-15T21:59:00+00:00

Ledig stilling opslået Indrykket for 21 dage siden
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