PhD scholarship in Causal-informed Machine Learning Methods for Climate Modelling DTU Compute

Do you want to do research on causal-informed machine learning methods for climate modelling

If you are establishing a career as a researcher in machine learning and are motivated to work with the latest generative models for climate modelling we can offer you a strong foundation. We seek a highly motivated and talented PhD student to join our group at DTU Compute offering a fully funded PhD scholarship (3-year employment) in a vibrant interdisciplinary research environment.

The position is part of the research project IcyAlert - Intelligent Climate Early Warning Alert for Arctic Ice-Free Summers funded under the NNF Grand AI Challenge programme.

You will become part of an enthusiastic team working closely with collaborators at DMI (Danish Meteorological Institute) and RMI (Royal Meteorological Institute of Belgium) to advance neural network-based methods for climate modelling.

About IcyAlert
The goal of the IcyAlertproject is to provide robust predictions of Arctic ice-free summers at multi-seasonal time scales and their potential climate impacts under various warming scenarios supporting sustainable climate solutions. Arctic summer sea ice extent has declined by nearly 13% per decade since 1979 accompanied by a 90% reduction of older ice. These changes pose increasing risks to ecosystems food security navigation etc.

By combining dynamical models satellite data causal analysis and explainable AI IcyAlert will identify critical thresholds enhance prediction accuracy and generate automated alerts for seasonal Arctic and extratropical climate events. Using Gefion in the project will be essential for analysing complex datasets and training advanced large-scale predictive models. This interdisciplinary initiative brings together experts in AI climate modelling and causal analysis to enhance preparedness for an ice-free Arctic.

Responsibilities and qualifications
This PhD project will focus on developing causal-informed machine learning (ML) methods to perform Arctic sea-ice predictions at multi-seasonal timescale. You will work with datasets such as CMIP6 ERA5 and CARRA2. There will be three subprojects: 1) quantify causal links between winter climate drivers (November-April) and summer Arctic sea ice area (May-October) 2) develop causal-informed AI/ML models for ice-free Arctic predictor 3) generate probabilistic predictions of ice-free conditions from 2030 to 2050.

You should have the following required skills:

• Have a strong background/interest in causal modelling (Math/Physics/Computer Science background).
• Demonstrated experience in machine learning including some exposure to graph modelling and diffusion models.
• Experience with implementing machine learning methods in Python using Pytorch or Flax.
• Familiarity with managing larger code bases and training models on HPC systems.
• Familiarity with climate modelling is a big plus.
• High level of motivation and creative problem-solving skills.
• Excellent communication and writing skills in English.

You must have a two-year masters degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year masters 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 seeDTUs rules for the PhD education.

Assessment
The assessment of the applicants will be made by Associate Professor Tommy Sonne Alstrøm and colleagues in the IcyAlert project.

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. Starting date is 1 November (or according to mutual agreement). The position is a full-time position.

You can read more aboutcareer paths at DTU here.

Further information
Further information may be obtained from Tommy Sonne Alstrøm .

You can read more about DTU Compute at .

If you are applying from abroad you may find useful information on working in Denmark and at DTU atDTU Moving to you have the option of joining our monthly free seminar PhDrelocation 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 than9 August 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 letter motivating the application (cover letter)
• Curriculum vitae
• Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

You may apply prior to obtaining your masters 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 Compute
DTU Compute Department of Applied Mathematics and Computer Science is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics data science computer science and computer engineering including artificial intelligence (AI) machine learning internet of things (IoT) chip design cybersecurity human-computer interaction social networks fairness and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world as a basis for the analysis design and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health green transition energy supply and life science. We collaborate with universities public and private organisations and companies in Denmark and abroad and through DTUs startup ecosystem we encourage innovation and entrepreneurship. We have a strong ethical human and sustainable approach that ensures integrity in our work. Therefore we strive for and take responsibility for driving the democratisation of digital technologies so that everyone has the opportunity to actively participate in the development and we ensure a continued open democratic and inclusive society for the benefit of all. At DTU Compute we value diversity inclusion and a flexible work-life more about us at.

DTU For the benefit of society since 1829
DTU is one of Europes 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 Europes 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 13800 students 1600 PhD students and 6500 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.