PhD students and projects

The PhD students and projects at the Gothenburg Research School of Health Engineering (GRSHE) are presented below. 

GRSHE is a joint graduate school for PhD students at Chalmers University of Technology, the Sahlgrenska Academy at the University of Gothenburg, and Sahlgrenska University Hospital. 


Almira Osmanovic Thunström

Affiliation: Sahlgrenska University Hospital, Institute of Neurology and Physiology.



Virtual Reality and Artificial Intelligence, evaluation of digital tools in Psychiatry

This project aims to explore different aspects of emerging technology as a tool in psychiatry. There are three main focuses in this project: 1) virtual reality for education and treatment, 2) brain computer interfaces for self-monitoring and neurofeedback and 3) chatbot technology with and without machine learning. The goal is to explore the effect of these three areas of non-pharmacological treatment strategies for affective disorders such as PTSD, anxiety, eating disorders and more. 


Alvin Combrink

Affiliation: Chalmers University of Technology, Electrical Engineering, Systems and Control



Optimal patient flows in the emergency care process

The objective of this research is to improve the operational efficiency within healthcare departments. This involves supporting healthcare personnel with tool for better informational awareness and planning of staff and patients. The results will reduce the time healthcare personnel spend on administrative work while improving the quality of decisions, and ultimately the quality of care.


David Hagerman Olzon

​Affiliation: Chalmers University of Technology, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing


Semi-supervised Learning for Medical Image Analysis

Most recent successes of machine learning have been based on Supervised Learning methods, fueled by large quantities of parallel compute power and humanly annotated training data. However, that option quickly becomes intractable due to the labour intensive work of manual annotation, especially for medical image data. Instead, many believe that Semi-Supervised Learning will drive the next AI revolution by using vast amount of unlabeled data (and some labeled examples) to discover all concepts and underlying causes that matter when interpreting an image. David's research is focused on developing new methods and techniques for Semi-Supervised Learning and apply it to medically relevant problems where lots of image data is available.


Eva Hagberg

Affiliation: University of Gothenburg, Sahlgrenska Academy, Institute of Medicine, Department of Molecular and Clinical Medicine


Deep learning algorithms for automated assessment of clinical echocardiography

Ultrasonography of the heart (echocardiography) is a cornerstone in cardiovascular clinical assessment and permits rapid assessment of cardiac structure and function. A typical echocardiography report includes demographic data, echocardiographic findings, measurements, and often a short summary. It takes months of training and experience to be able to evaluate a basal echocardiography examination properly and for the difficult and complicated cases, years of training may be needed. Eva’s research involves developing deep learning methods for classification of the echocardiography images as well as natural language processing methods for automatic image annotation based on health records. The aim is thus to develop fully automated software, aiding in echocardiography assessment in clinical practice.


Frans Stålfelt

Affiliation: University of Gothenburg, Sahlgrenska Academy



Assessments of technical advancements for prevention of surgical site infections in orthopedic implant surgery

The PhD project aims to test, measure, and evaluate different technical advancements and methods, which can be used inside operating rooms as preventive actions against surgical site infections. Several studies included in the doctoral thesis are in collaboration between University of Gothenburg/Sahlgrenska Academy and Chalmers University of Technology.


Madeleine Liljegren

Affiliation: University of Gothenburg, Sahlgrenska Academy, Institute of Medicine, School of Public Health and Community Medicine


Health promoting qualities in the outdoor environment at residential care facilities for older persons – from mapping to working method for design and planning

The aim of the PhD-project is to increase the knowledge of health promoting qualities in the outdoor environment at residential care facilities for older persons, which is an identified need in urban planning. The project will result in: 1.) an evidence-based guideline that focuses on important aspects of qualities in the physical outdoor environment and 2.) a working method that supports implementation of the critical health promoting qualities in design and planning. The research project comprises the first PhD-project in Sweden between caring science (University of Gothenburg) and architecture (Chalmers University of Technology).


Michael Kercsik

Affiliation: University of Gothenburg, Sahlgrenska Academy, Wallenberg Laboratory for Cardiovascular and Metabolic Research 


Detailed quantitative radiological image analysis of coronary artery disease using machine learning.

In this research project, we aim to develop and study the possibilities of machine-learning assisted analysis of coronary arteries, coronary artery disease, and the relationship with epicardial adipose tissue on atherosclerosis detected using coronary computed tomographic angiography (CCTA). To this end, Michael studies how manually annotated CCTA data relates to cardiovascular risk factors, the relationships between epicardial and percoronary fat tissue with the atherosclerotic plaque burden. In parallel, a convolutional deep learning software will be developed in order to detect, outline, and segment coronary artery anatomy and plaque lesions in order to support the analysis of CCTA images. 


Roman Naeem

Affiliation: Chalmers University of Technology, Electrical Engineering, Imaging & Image Analysis



Semi-supervised Learning for Medical Image Analysis

Roman Naeem is a PhD student in the Gothenburg Research School of Health and Engineering, focusing his research on semi-supervised learning for medical image analysis. The research deals with implementing algorithms and models to analyze medical images like Computed Tomography Angiography (CTA) images without any manual annotations.



Seyed Moein Pichnamaz

Affiliation: Chalmers University of Technology, Electrical Engineering, Biomedical Electromagnetics    



Microwave methods applied to biomedical diagnostics 


Seyed Moein Pishnamaz is a PhD student in the Biomedical Electromagnetics research group, focusing his research on microwave methods applied to biomedical diagnostics. Moein is involved in a project aimed to develop a fast and portable prototype for microwave diagnostics of stroke for use in the prehospital decision-making process. His work will mainly focus on implementing, testing, and verifying the experimental system in phantom models, animal models, and also in patients.


Therese Klang

Affiliation: University of Gothenburg, Sahlgrenska Academy, Institute of Medicine, School of Public Health and Community Medicine, Occupational and environmental medicine


Chimney sweeps exposure to soot containing PAH

Chimney sweeps have an increased risk for different severe health outcomes and the risk has been connected with their exposure to soot. To be able to improve the preventive measures, more knowledge is needed concerning the exposure, which is the aim of this project. In the project we will use different methods to measure the exposure, including a new method, a sensor that measures black carbon.


Tomás Gómez Vecchio

Affiliation: University of Gothenburg, Sahlgrenska Academy, Institute of Neuroscience and Physiology, Clinical Brain Tumor Research Group


Patient-reported outcomes and imaging analyses in patients with low-grade glioma

The project has two specific aims: a) to evaluate the trajectories of health-related quality of life during glioma treatment; b) to use artificial intelligence with the intention to predict glioma molecular profile and patient survival from clinical images.

Two study designs are tailored for these aims: a) a prospective observational study using quality of life questionnaires prior to treatment and at follow-up; b) a retrospective study applying machine-based learning using pseudonymized annotated images.

The project is coordinated by the Clinical Brain Tumor Research Group at the Institute of Neuroscience and Physiology of the University of Gothenburg. Its goal is to contribute to the improvement of the clinical management together with the oncological and functional outcomes in patients with low-grade glioma.


Vilma Canfjorden

Affiliation: University of Gothenburg, Sahlgrenska Academy, Institute of Biomedicine, Department of Laboratory Medicine 


Quantum computing and life science - Using quantum algorithms within genomics for diagnostics, prediction and prevention 

The project focus on finding quantum computing applications within existing diagnostic flows and bioinformatics. Hopefully, the project will give rise to novel informatics tools that use a completely new approach to computing as well as plans for future, wider applications that can be implemented as the technology matures.

​​​​​​​​

Page manager Published: Fri 13 May 2022.