Postdoctoral in physics-driven machine learning for transient electromagnetic data for subsurface imaging

Join us at the Department of Electrical and Computer Engineering at Aarhus University for a two-year postdoctoral position focused on physics-driven machine learning for ground-based, airborne, and drone-based transient electromagnetic (TEM) data. The project will build on anEMone, our fully differentiable TEM forward-modelling framework implemented in PyTorch, to develop supervised and self-supervised deep learning models that directly incorporate electromagnetic physics. We are looking for a motivated postdoc to lead cutting-edge research that combines methodological development with practical deployment within a large Innovation Fund Denmark Grand Solutions project.

Expected start date and duration of employment

This is a 2-year position from January 1, 2027, or as soon possible.

This is a fixed-term position to end 24 months after the start date.

Job description

The postdoc position focuses on physics-driven machine learning methods for ground-based, airborne and drone-based transient electromagnetic (TEM) data. The work will build on anEMone, our fully differentiable TEM forward-modelling framework implemented in PyTorch, and use the electromagnetic physics to develop supervised and self-supervised deep learning models for TEM modelling and inversion.

Your key responsibilities will include:

• Developing supervised and self-supervised deep learning models for TEM inversion that incorporate our fully differentiable TEM forward operator.
• Designing physics-based loss functions and training strategies that enforce consistency between measured data, predicted subsurface models and simulated TEM responses.
• Incorporating system geometry, transmitter waveforms, measurement uncertainties and motion-related information into the learning frameworks.
• Training and validating the developed methods using synthetic and field TEM datasets.
• Developing computationally efficient implementations suitable for near-real-time or real-time applications.
• Publishing high-impact research and contributing to outreach and dissemination activities.

The postdoc will have considerable freedom to shape the methodological direction of the research while contributing to the project’s overall objective of developing robust and deployable physics-driven machine learning solutions for TEM data.

In addition to the colleagues at Department of Electrical and Computer Engineering, you will collaborate closely with the geo-physicists from the Hydrogeophysics Group (HGG) at the Department of Geoscience at Aarhus University, and with TEMcompany, our industrial project partner. You will also have the opportunity to supervise bachelor’s and master’s students.

Your profile

We are looking for a highly motivated candidate with a strong background in deep learning, scientific machine learning or in-verse problems. The ideal candidate thrives in interdisciplinary settings and is interested in developing methods that combine physical modelling with real-world geophysical applications.

Required qualifications include:

• PhD in electrical engineering, computer engineering, computer science, applied mathematics, geophysics, physics or a related field.
• Documented experience with deep learning model development (e.g., CNNs, UNets, Transformers).
• Strong programming skills in Python and hands-on experience with deep learning frameworks in PyTorch.
• Experience with scientific computing, numerical modelling or inverse problems.
• Strong publication record relative to career stage.
• Excellent written and spoken English communication skills.

Following qualifications will be considered as an advantage:

• Experience with physics-informed or physics-driven machine learning.
• Knowledge of electromagnetic methods, particularly TEM modelling or inversion.
• Experience with geophysical or other scientific datasets.
• Experience developing computationally efficient models for near-real-time or real-time applications.
• Experience working in collaborative and interdisciplinary research environments.

Who we are

You will be based at the Department of Electrical and Computer Engineering (ECE) at Aarhus University, a dynamic and growing department committed to excellence in research, education, and innovation. Our research spans signal processing, machine learning, digital twins, and intelligent systems — all with a strong emphasis on real-world impact and societal relevance.

This position is anchored within the Signal Processing and Machine Learning section at ECE and will be mentored by Assistant Professor Muhammad Rizwan Asif, whose research focuses on developing advanced deep learning methods for geoscientific and environmental applications. Current research includes machine learning for transient electromagnetic data, groundwater mapping, remote sensing and the integration of physical modelling with data-driven methods.

Your daily work will be closely linked to researchers in the Hydro-geophysics Group (HGG) at the Department of Geoscience, an internationally recognized leader in the development and application of transient electromagnetic methods for subsurface mapping. You will also collaborate closely with TEMcompany, the industrial project partner, providing direct access to ground-based and drone-based TEM systems, field datasets and operational processing workflows.

What we offer

We offer a vibrant and inclusive research environment with a strong interdisciplinary foundation and a clear commitment to real-world impact. Denmark is consistently ranked among the best countries in the world for work-life balance and quality of life. Family-friendly policies include generous parental leave, subsidised childcare, and access to excellent public healthcare and education. As a postdoc at Aarhus University, you will benefit from a supportive and flexible workplace culture that values diversity and offers excellent conditions for researchers and their families.

Specifically, we offer:

• A vibrant, interdisciplinary work environment that encourages collaboration across different domains
• Access to state-of-the-art facilities and computing infrastructure
• Strong support for research career development, including mentoring and international networking opportunities
• A commitment to diversity, equity, and inclusion in all aspects of our work
• A high degree of flexibility and autonomy in planning your research activities
• A workplace characterised by professionalism, equality and a healthy work-life balance.
• Non-financial relocation support and assistance with practical matters such as housing, childcare, and integration into Danish society.

We warmly welcome applications from all qualified candidates and strongly encourage women and individuals from un-derrepresented backgrounds in STEM to apply.

Place of work and area of employment

The place of work is Finlandsgade 22, 8200, Aarhus N, and the area of employment is Aarhus University with related departments.

Contact information

For further information, please contact: Assistant Professor Muhammad Rizwan Asif, +4560909831, rizwanasif@ece.au.dk

Deadline

Applications must be received no later than September 10, 2026.

Ensuring gender balance at the Department of Electrical and Computer Engineering is a high priority at Aarhus University, and therefore, we particularly encourage women to apply for this position. No candidate will be given preferential treatment, and all applicants will be assessed on the basis of their qualifications for the position in question.

Application procedure

Shortlisting is used. This means that after the deadline for applications – and with the assistance from the assessment committee chairman, and the appointment committee if necessary – the head of department selects the candidates to be evaluated. All applicants will be notified whether or not their applications have been sent to an expert assessment committee for evaluation. The selected applicants will be informed about the composition of the committee, and each applicant is given the opportunity to comment on the part of the assessment that concerns him/her self.

Formalities and salary range

Technical Sciences refers to the Ministerial Order on the Appointment of Academic Staff at Danish Universities under the Danish Ministry of Science, Technology and Innovation.

The application must be in English and include a curriculum vitae, degree certificate, a complete list of publications, a statement of future research plans and information about research activities, teaching portfolio and verified information on previous teaching experience (if any). Guidelines for applicants can be found here.

Appointment shall be in accordance with the collective labour agreement between the Danish Ministry of Taxation and the Danish Confederation of Professional Associations. Further information on qualification requirements and job content may be found in the Memorandum on Job Structure for Academic Staff at Danish Universities.

Salary and terms as agreed between the Danish Ministry of Taxation and the Confederation of Professional Unions at basic salary steps 4-8.

Aarhus University’s ambition is to be an attractive and inspiring workplace for all and to foster a culture in which each individual has opportunities to thrive, achieve and develop. We view equality and diversity as assets, and we welcome all applicants.

Research activities will be evaluated in relation to actual research time. Thus, we encourage applicants to specify periods of leave without research activities, in order to be able to subtract these periods from the span of the scientific career during the evaluation of scientific productivity.

Aarhus University offers a broad variety of services for international researchers and accompanying families, including relocation service and career counselling to expat partners.

Aarhus University also offers a Junior Researcher Development Programme targeted at career development for postdocs at AU.

The application must be submitted via Aarhus University’s recruitment system, which can be accessed under the job advertisement on Aarhus University's website.