Aarhus Universitets segl

Ph.d. ved Institut for Elektro- & Computerteknologi

Når du starter på din ph.d-uddannelse ved Institut for Elektro- og Computerteknologi (ECE) bliver du en del af The Graduate School of Technical Sciences på Aarhus Universitet, og du får en af verdens bedste forskeruddannelser.

På instituttet møder du en kultur, der er præget af diversitet og nysgerrighed med plads til at være studerende, underviser, forsker og menneske. 

Vi beskæftiger os både med grundvidenskabelige problemstillinger og anvendt forskning meget ofte i samarbejde med danske og internationale virksomheder. Du kan se eksempler på ph.d.-projekter nedenfor. 

Vi vokser, og vi kan dermed tilbyde nogle unikke karrieremuligheder for dygtige medarbejdere, der er drevet af et ønske om at skabe impact med deres forskning. 

Herunder kan du møde nogle af vores ph.d studerende og læse om deres igangværende projekter

Asbjørn vil give blinde synet tilbage med solceller

Ved hjælp af mikroskopiske solceller, som opereres ind bagerst i øjet, håber Asbjørn, at han i sit ph.d.-projekt kan genskabe noget af evnen til at se hos patienter øjensygdomme.

Det, Asbjørn forsøger at gøre, er at indoperere bittesmå solceller bagerst i grisens øjne og få dem til at udsende elektriske impulser, når lyset rammer dem.

Ideen er, at solcellerne skal overtage opgaven efter de fotoreceptor-celler, der ikke længere fungerer hos patienter med sygdommen Retinitis pigmentosa.

I et raskt øje rammer lyset fotoreceptorerne, som udsender et elektrisk signal, der bliver samlet op af neuronerne lige foran. De elektriske signaler rejser gennem hjernen og hen til de visuelle centre, som oversætter dem til de billeder, vi ser af verden.

Det er lykkedes forskere andre steder i verden at indoperere et lignende implantat i patienter. De fik endda også noget af synet tilbage, forklarer Asbjørn.

Digital Twin Deployment for Enhanced Autonomy and Scalability of Ready-Mix Concrete Batch Plants

This project is centered around digital twins, and the application of formal methods for predictive maintenance and anomaly detection for mobile concrete batching plants (MCBP).

Sensors will be placed within the MCBP to collect real-time data, such as operational and performance metrics for monitoring. This would enable us to make data-driven decisions and state predictions, thus bridging the gap between the physical and digital systems.

Anomaly detection is driven by data, analytics, and machine learning. These algorithms continuously monitor the data for deviations from defined patterns. This is a proactive approach that helps minimize downtime and optimizes the maintenance and operational efficiency of the MCBP.

In summary, the project will use technology to transform a manual industry to become digitalized, thus enhancing efficiency and reducing costs for both the production and delivery of the concrete and the MCBP itself.


Project title: Digital Twin Deployment for Enhanced Autonomy and Scalability of Ready-Mix Concrete Batch Plants

PhD student: Mikkel Schmidt Andersen

Contact: msa@ece.au.dk

Project period: 15.02.2024 - 14.02.2027

Main supervisor: Peter Gorm Larsen

Co-supervisor(s): Cláudio Gomes & Michael Sandberg

Determining the Limits of Self-Adaptive Behaviour of Robots from a Safety

Modern robotic systems must adapt to drastic and unprecedented changes in their environment at runtime, i.e., they must be self-adaptive.

The RoboSAPIENS project, which my PhD is a part of, explores creating an architecture for trustworthy self-adaptation. This entails that the system successfully adapts to new environments whilst maintaining safe behavior, which is not a trivial task.

On one hand, the exact behavior of a self-adaptive system is difficult to determine, as it changes depending on the environment it is operating within. On the other hand, robotics are typically verified as being safe through certifications based on their exact behavior. Thus, we need to find a way to ensure that the robot still lives up to its safety requirements despite changing its behavior to accommodate changes to the environment.

My research will be aimed at contributing towards this objective through three main tasks:

  1. Domain Analysis and Ontology Development: Collaborate closely with industrial partners to create an ontology capturing the commonalities and differences in verification requirements across domains.
  2. Language Definition and IDE Development: Define domain-specific language(s) to describe safety procedures within adaptation loops.
  3. Formally Verified Trustworthiness Checkers: Generate formally verified trustworthiness checkers and accompanying documentation. 


Project title: Determining the Limits of Self-Adaptive Behaviour of Robots from a Safety

PhD student: Morten Haahr Kristensen

Contact: mhk@ece.au.dk

Project period: 02/2024 - 02/2027

Main supervisor: Peter Gorm Larsen

Co-supervisor(s): Cláudio Ângelo Gonçalves Gomes & Lukas Esterle

Distributed Edge Intelligence for Immersive Tactile Internet

My research work focuses on contributing to the Tactile Internet. Tactile Internet is the foundation of the use cases such as Teleoperation, and Immersive Virtual Reality on the table. The key to the Tactile Internet is its ability to transmit information quickly, ensuring that users interacting with devices on the network feel like it's happening in real-time.

Different strategies are proposed to provide the users with an excellent experience in interacting with endpoints. One of which is called “distributed intelligence”. To minimize the latency, local edge devices are organized to work together to offload the computation remote cloud. In that way, the workload on the cloud is reduced thus improving overall performance.

Through this localized intelligent processing, the goal is to make user interactions smoother and more natural.


Project title: Distributed Edge Intelligence for Immersive Tactile Internet

PhD student: Yuchen Gao

Contact: yuchen@ece.au.dk

Project period: 15-01-2024 to 14-01-2027

Main supervisor: Qi Zhang

Co-supervisor: Daniel Enrique Lucani Rötter

Improving healthcare with digital screening and monitoring support tools

Extensive screening and long-term home monitoring programs have proven effective in reducing the impact of diseases. The introduction of screening pregnant women and initiating medical treatment for high-risk women holds the potential to mitigate the effects of pregnancy-related hypertensive disorders. Annually, these hypertensive disorders contribute to half a million maternal deaths worldwide. However, the expenses associated with implementing such screening are currently hindering the adoption in hospitals, with staff resources emerging as a critical factor.  

This project aims to enhance healthcare quality, efficiency, and cost-effectiveness in hospital and home settings by implementing sensor-based self-measuring stations, monitoring support tools, and personalized medicine. The ultimate goal is to establish automated screening, digital coaching, and decision support tools to assist in screening and initiating personalized medicine to mitigate the impact of pregnancy-related hypertensive disorders.


Project title: Improving healthcare with digital screening and monitoring support tools

PhD student: Louise Thostrup Pedersen

Contact: ltp@ece.au.dk

Project period: 01.01.2024 - 31.12.2026

Main supervisor: Stefan Rahr Wagner

Hybrid photovoltaic and optogenetics stimulation of the neuroretina for restoring visual function in blind patients

In diseases causing blindness by photoreceptor degeneration, such as retinitis pigmentosa and age-related macular degeneration, the neuronal circuit in the eye remains intact and functional. Two approaches to restore visual function are photovoltaic (PV) retinal implants and optogenetics (OG).
The PV implants are micro-scale solar cell units which when illuminated produce a small current. The neuroretina consists of neuron layers in the retina that propagate the signal from the photoreceptors to the visual cortex in the brain. When implanted in close proximity to the neuroretina the currents of the PV implant activate the neurons locally, thereby mimicking the photoreceptor signals, which then can be perceived as an image in the brain.

In OG viruses are used to deliver DNA encoding light-sensitive proteins to the neuroretina, often targeting the retinal ganglion cells. The neurons can then be stimulated directly by illumination as the light-sensitive proteins induce their activation.
Both PV implants and OG have been tested separately, but in this novel project we aim to combine them to restore visual function further than the two approaches can discretely. To achieve this aim micro solar cells and viruses are designed and produced and tested individually in ex vivo experiments with pig eyes. Subsequently, animal studies will be performed to assess the combination treatment in vivo in a disease model.
Furthermore, the project aims to develop a controlled drug delivery method for the optogenetics component that ensures safety, quantity, and area of delivery.


Project title: Hybrid photovoltaic and optogenetics stimulation of the neuroretina for restoring visual function in blind patients

PhD student: Asbjørn Cortnum Jørgensen

Contact: acj@ece.au.dk

Project period: 01.08.2023 – 31.07.2026

Main supervisor: Farshad Moradi

Co-supervisor(s): Rasmus Schmidt Davidsen

Next Generation, People-Centred Analysis Tools for Holistic, Sustainable Renovation of Green Buildings and Neighbourhoods (GBN)

The significance of improving the sustainability of buildings has been increasing rapidly over the recent years. While it is important to analyze the technical aspects that might be related to the impacts of buildings and neighborhoods on our environment, it is crucial to consider the social criteria in the context of sustainable building environment.

In this project, we investigate the importance of social criteria in the building environment.

In addition, we develop and implement a decision support software solution in order to provide an assessment methodology. This methodology can verify the satisfaction of social aspects that directly affect the well-being of the buildings’ occupants such as indoor air quality, visual and thermal comfort.  

This research will be carried out on actual buildings that are part of the newly renovated AU living lab, as an integration with the EU-funded PROBONO project, which aims to provide strategic planning tools from technical, environmental, economic and social perspectives to create recommendations and standardization actions for the European construction industry.


Project title: Next Generation, People-Centred Analysis Tools for Holistic, Sustainable Renovation of Green Buildings and Neighbourhoods (GBN)

PhD student: Yazan Nidal Hasan Zayed

Contact: yazan@ece.au.dk

Project period: 15.11.2022 - 14.11.2025

Main supervisor: Carl Schultz

Co-supervisor: Aliakbar Kamari

Privacy-by-design compression, sharing, and processing of IoT data in IoTalentum architecture

To design, implement and evaluate secure data sharing and controlled, privacy-preserving processing of IoT data using the cloud-to-thing IoTalentum infrastructure, trusted execution environments (e.g., Intel SGX), local computing resources, and judicious allocation of data. To investigate compression techniques that allow for processing directly on compressed data as a way to reduce data traffic and memory usage, both in local, MEC, and cloud devices in order to address the explosion in generated data worldwide.


Project title: Privacy-by-design compression, sharing, and processing of IoT data in IoTalentum architecture

PhD student: Francesco Taurone

Contact: francesco.taurone@ece.au.dk

Project period: July 2022 – June 2025

Main supervisor: Daniel E. Lucani Rötter

Co-supervisor: Qi Zhang

The Neuronal Correlates of Speech Intelligibility (NCSI)

The number of hearing-impaired individuals is growing rapidly on a global scale, due to the increase of the elderly population. Hearing impairment can make it difficult to communicate with the world around us, and it can be so difficult that certain social situations are avoided, which can lead to isolation, cognitive decline and even depression. Therefore, it is no great surprise that how well a person can understand speech (speech intelligibility) has been a high priority when developing and fitting hearing aids. The golden standard for determining speech intelligibility is a behavioral test, where the hearing-impaired individual is introduced to speech elements and repeats back what is heard. This is not only a highly time-consuming process, but also a very subjective procedure, that has shown not to always correspond to the users experience in their daily lives.  

Can we measure speech intelligibility using electroencephalography?
This project aims to investigate whether it is possible to measure how well speech is understood using electroencephalography (EEG). If this is possible the immediate benefit will be obtaining a new objective measure to evaluate whether for example a hearing aid improvement is enhancing speech intelligibility in a lab setting. The next natural step will be to investigate whether it is possible to measure speech intelligibility using ear-EEG. Ear-EEG is a device used for recording electrophysiological signals using electrodes placed inside the ear. The benefit of using the ear-EEG is that it is possible to measure EEG in the natural environment of the user, in an unobtrusive and mobile manner. This hopefully resulting in a better speech intelligibility for the end user, and thereby closing the communication gap caused by their hearing impairment.


Project title: The Neuronal Correlates of Speech Intelligibility (NCSI)

PhD student: Heidi Bliddal

Contact: hebl@ece.au.dk

Project period: 01.03.2022 - 01.03.2025

Main supervisor: Preben Kidmose

Co-supervisor: Emina Alickovic, Johannes Zaar and Christian Bech Christensen

Using deep learning methods to tailor sleep scoring to specific populations

Automatic sleep scoring make use of algorithms inferring sleep states from sleep recordings. In recent years, these algorithms have shown great performance, even reaching human performance, by employing various deep learning approaches. Most of these models are trained on recordings of healthy patients or a few specific patient groups, and they can not be expected to generalize to other patient groups as sleep differs with both age and health. In addition, sleep EEG recordings are expensive to produce and label for training.

In this project, we will seek to develop and test “transfer learning” or “semi-supervised learning” approaches to this problem, adapting high performing models trained on large data sets to perform well on smaller data sets recorded for different patient groups or with a different device.

In particular, the developed methods will be used not only for regular, clinical sleep recordings, but also for recordings made using the ear-EEG device developed at the Center for Ear EEG.


Project title: Using deep learning methods to tailor sleep scoring to specific populations

PhD student: Kristian Peter Lorenzen

Contact: kpl@ece.au.dk

Project period: 2022 - 2025

Main supervisor: Preben Kidmose

Co-supervisor: Kaare Mikkelsen

Shortening time to market for product design using simulation and traceability in a digital twin context

This project is carried out with close collaboration with the Danish companies Vestas, Velux, Millpart, Hydrospecma, and Landia, and the Ringkøbing-Skjern municipality.

Each company focuses on different products and industries. The goal of this project intends to contribute to digital technologies of the involved companies for improving product design and production using virtualization and simulation methods, considering strategies to enhance optimization and efficiency of the processes.

This will be achieved through the combination and integration of already existing tools for digital twin representation and software for process and product management. This PhD project is associated with the Digital Transformation Lab (DTL) in Ringkøbing-Skjern Municipality targeting a digital transformation of the 5 companies in the area. The DTL will be used for the experimental parts of this PhD project. The DTL will be key in ensuring the best exchange of research-based knowledge from the university with practice-based knowledge at local companies.


Project title: Shortening time to market for product design using simulation and traceability in a digital twin context

PhD student: Santiago Gil Arboleda

Contact: sgil@ece.au.dk

Project period: 01.06.2021 – 31.05.2024

Main supervisor: Peter Gorm Larsen

Co-supervisor: Alexandros Iosifidis

Detecting attended auditory events using ear-EEG: a new approach to auditory attention decoding

Auditory Attention Decoding (AAD) is envisioned to become an important technology for the next generations of hearing aids. With an AAD component, a hearing device will be able to detect which sound sources the user is attending, and thereby the audio signal processing in the hearing device can be adapted accordingly.

In this project, we propose a new approach in which the AAD is based on cognitive processing of the audio event stimuli. This is expected to better separate attended and non-attended sound sources. The approach try to extract event related potentials and analyze using state-of-the-art deep learning model. We will validate the algorithm on Ear-EEG signal which is minimalistic and will allow the  technology to be implemented in future hearing aid devices.


Project title: Detecting attended auditory events using ear-EEG: a new approach to auditory attention decoding

PhD student: Nhan Duc Thanh Nguyen

Contact: ndtn@ece.au.dk

Project period: 01-06-2021 - 31-05-2024

Main supervisor: Preben Kidmose

Co-supervisor: Kaare Mikkelsen

Joint processing of ear-EEG and ear-fitted body-coupled microphone signals

Physicians are typically concerned with sounds originating from the body, which are traditionally listened to through an acoustic stethoscope. However, interpretation of sounds through such instruments is subject to the training of the individual physician. To overcome this challenge and further advance the method, an electronic version of the stethoscope has been invented, allowing for more objective interpretation of such sounds.

Ear-EEG is a technique specifically designed to monitor brain activity during everyday activities. Through an ear-fitted device, physiological signals reflecting the subject’s brain activity are measured.

This project aims to extend the ear-EEG platform with a body-coupled microphone, i.e. an electronic stethoscope in the ear, enabling recording in real-life environments with a discrete and unobtrusive wearable device. Furthermore, we seek to explore methods for joint processing of ear-EEG signals and sounds picked up from the body-coupled microphone . This would allow for a broader understanding of the current state of the body and is highly applicable in both research and in medical devices for health monitoring.

This project is carried out at the newly founded Center for Ear-EEG


Project title: Joint processing of ear-EEG and ear-fitted body-coupled microphone signals 

PhD student: Bjarke Lundgaard Gårdbæk 

Contact: blg@ece.au.dk

Project period: August 2020 to July 2025

Main supervisor: Preben Kidmose 

Frequency conversion and stabilization in photonic integrated circuits

I am researching in optical nonlinearities to design a robust and scalable laser device with narrow linewidth for emission at various wavelengths. Nonlinear optics allows amongst other interesting effects, for wavelength conversion. This can be used for realizing lasers, at wavelengths otherwise hard to come by and have been used to achieve narrow linewidth. Implementing nonlinear optics in photonic integrated circuits (PIC’s) can enhance the nonlinear effects, while making it robust and small. This could for example allow for handheld instruments to measure and identify gasses in exhausted breaths for medical analysis.


Project title: Frequency conversion and stabilization in photonic integrated circuits 

PhD student: Emil Zanchetta Ulsig 

Contact: ulsig@ece.au.dk

Project period: Nov 2020 to Aug 2024

Main supervisor: Nicolas Volet 

ReMaRo (Reliable AI for Marine Robotics)

The ReMaRo ETN project aims to develop Artificial Intelligence methods for  submarine robotics with quantified reliability, correctness specifications, models, tests, and analysis & verification methods.

Within this project, I am involved in the development of deep learning algorithms for vision-based navigation for underwater safety critical applications.  One of the underwater contexts contemplated within the projects is pipeline inspection. That is, I will develop visual (camera-based) localization algorithms for pipeline inspection, supported by other sensors such as sonars. Moreover, I will work on the reliability assessment of these methods with the help of other PhD students from the ReMaRo consortium to identify whether if the localization measurements are reliable or not depending on the quality of the measurements taken.


Project title: ReMaRo (Reliable AI for Marine Robotics)     

PhD student: Olaya Álvarez-Tuñón

Contact: olaya@ece.au.dk

Project period: 2020 – 2024

Main supervisor: Erdal Kayacan