Postdoctoral Fellowship (12) at Aalborg University, Denmark

The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for a position as research assistant or postdoc in the field of Novel AI based perception & Control for robots as per February 1, 2026, or as soon as possible thereafter. The position is available for a period of  1 year.

In electronic engineering, Aalborg University is known worldwide for its high academic quality and societal impact. The Department of Electronic Systems employs more than 200 people, of which about 90 are PhD students, and about 40 % of all employees are internationals. In total, it has more than 600 students in its BSc and MSc programs, which are based on AAU’s problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results into applications in close collaboration with industrial partners worldwide.

This postdoc position is financially supported by the VILLUM Synergy project entitled ReBatMan: A New Paradigm for Sustainable Battery Lifetime Prediction through Acoustic Sensing and Machine Learning. The project is headed by Assoc. Prof. Daniel-Ioan Stroe, Department of Energy, and Prof. Jan Østergaard, Department of Electronic Systems, Aalborg University. 

Batteries are vital for the green transition, enabling electrification of transportation and integration of renewable energy. Despite falling costs, uncertainties about battery lifetime challenge sustainability. Accurate lifetime prediction is crucial for effective management, enhancing performance, and preventing failures like fires or explosions. Current prediction methods mainly rely on extensive lab testing and modeling, using insights from destructive post-mortem analyses to improve accuracy. However, this approach is complex and unsustainable due to high energy consumption for parameterization. To tackle this sustainability challenge, a cross-disciplinary collaboration is dedicated to the project, uniting experts in battery technology and acoustic signal processing and machine learning. The goal is to harness advanced data science techniques to establish a novel paradigm for online non-invasive battery lifetime prediction through acoustic emission detection. This involves modeling the battery as a 3D acoustic landscape and tracking its changes over time. The expected outcomes are groundbreaking for battery management and will make substantial advances in online explainable data-driven prediction for time series data.

The specific tasks of this postdoc position will be to use active acoustic probing techniques based on ultrasonic signals that are transmitted through the battery. The resulting return signals are then analyzed using advanced signal processing and machine learning. The goal is to quantify both the extent of material degradation and its precise spatial distribution within the battery structure. By modeling the battery as a dynamic 3D acoustic landscape, we expect to be able to track temporal and spatial changes that reflect internal degradation processes.   

The research area will be within design and modeling of pulsed power supplies utilizing power electronics technology for plasma generation.
The successful application prerequisite is that you demonstrate a documented expertise in most of the areas below:

• Proven-research experiences in at least three of the following research topics:
  1) Wide-band gap (WBG) based Resonant and non-resonant based power supply design
  2) Ultra efficient point of load power supplies for cell level design
  3) Adaptive pulse rise/fall time control
  4) Modular power electronic converter design for scalable design
  5) Electromagnetic compatibility and filter designing in power electronics
  6) Multiphysics and time-frequency modeling of electric field

This position is part of the cross-disciplinary DK-Future project – Probabilistic Geospatial Machine Learning for Predicting Future Danish Land Use under Compound Climate Impacts, funded by the Villum Foundation (Synergy Programme). Two postdoctoral researchers will collaborate across AAU’s Departments of Computer Science (CS) and Sustainability and Planning (PLAN).

This position focuses on the machine learning methodology of the project, aiming to:

• Develop probabilistic spatio-temporal models that integrate uncertainty from climate projections into land-use forecasts.
• Advance Bayesian and ensemble learning approaches for non-stationary temporal processes.
• Implement probabilistic diffusion or generative models for long-term forecasting.
• Collaborate closely with the geoinformatics team to ensure models integrate effectively with geospatial data and concepts.

• Proven-research experiences in the following research topics:
  1)Isolated power converter topologies (e.g., Dual-active-bridge, LLC, and resonant converters).
  2)Power converter design and optimization.
  3)Multi-domain modeling simulation (e.g., electro-thermal).
  4)Wide-band-gap power semiconductor devices.
  5)Efficiency and power loss analysis of active and passive devices including magnetics.
  6)Digital twin modeling of power electronics including control and modulation.
• Familiarize with at least two of the following topics:
  1)Reliability and lifetime modeling of power electronics.
  2)Time-frequency domain EMI modeling of power electronic converters.
  3)Electromagnetic compatibility and filter design in power electronics.
• Proven-records in peer-reviewed publications in the most recognized power electronics journals.
• Hands-on experiences in experiment development and testing (e.g., hardware development,
  software implementation in various processor platforms) is required.
• In-depth understanding of power electronics and its engineering constraints is required.
• Strong English communication skills is mandatory.
• Experiences in assisting teaching and supervision is a plus.

Your application must include the following: 

• Application, stating reasons for applying, qualifications in relation to the position, and intentions and visions for the position 
• Curriculum Vitae (CV) 
• Diplomas (master’s degree diploma and PhD diploma) 
• List of publications, with an indication of the attached publications that you wish to be taken into account in the assessment. You may attach a maximum of ten publications. If you are applying for a position at The Faculty of Social Sciences and Humanities (SSH), you must submit a co-author statement, see template for co-author statement 
• Documentation of teaching qualifications. Depending on which faculty you are applying for employment, please see guidelines for teaching portfolio at The Technical Faculty of IT and Design, The Faculty of Engineering and Science and The Faculty of Medicine or guidelines for teaching portfolio at The Faculty of Social Sciences and Humanities. If this is not enclosed, the applicant must enclose the reasons for this .
• Dissemination qualifications, including participation on committees or boards, participation in organisations etc. 
• Additional qualifications in relation to the position 
• References/recommendations 

You will join the Grand Solution project ‘DRONES: Drone-Obtained Electromagnetic Signatures’ to exploore innovative approaches such as drone-based radio testing for large objects and EM signiture characterization in complex in-situ environments. The key responsibility of the position is to develop post-processing methods to extra essential features from the collected measurement data despite drone positional inaccuracy, irregular sampling grids, variations in measurement conditions, and other measurement uncertainties.

The responsibilities of a research assistant involve developing technical solutions aligned with the consortium’s objectives and contributing to related research areas. For a postdoctoral researcher, there is a stronger emphasis on initiating and advancing independent research that both supports the project and contributes meaningfully to the broader scientific field

Background. Managing global water resources is one of the most pressing challenges of our time, especially under the growing pressures of climate change and human activity. Global hydrological models are vital tools, but they often fall short due to data limitations and model simplifications. Recent breakthroughs in data assimilation (DA) and machine/deep learning (ML/DL) are changing the game. With new satellite missions like SWOT and upcoming Sentinel-6A/6B, we now have unprecedented access to global water observations. The challenge of turning these data into actionable insights will be addressed in the proposed Postdoc project.

The topic of this Postdoc position is the development of data-driven methods to estimate river streamflow using satellite altimetry. Particularly, the Postdoc will explore hybrid deep learning models that integrate river network information, creating a hydraulically informed AI approach that pushes the boundaries of streamflow estimation.

Som postdoc får du ansvar for dataindsamling blandt naturorganisationer og andre interessenter i lokale treparter. Du får ansvar for analyse af interne samarbejdsrelationer, analyse af videns- og kapacitetsopbygning blandt civilsamfundsaktører samt analyse af koalitionsdannelser i trepartsfeltet. Du må påregne, at arbejdsopgaver kan ligge geografisk spredt i hele Danmark, og at der vil være rejseaktiviteter forbundet med stillingen. Der er undervisningsopgaver knyttet til stillingen.