Current PhD vacancies

RNA modifications control the stability, turnover, immunogenicity and translatability of RNA molecules. RNA modifications can be dynamically regulated with consequences on normal and pathological physiology. Adenosine deaminations are widespread and affect the immunogenicity of RNAs as they are recognized by pattern recognition receptors. Moreover, adenosine deaminations lead to the formation of inosines which are recognized as guanosines by most cellular machineries. Thus, A to I exchanges can recode RNAs which alters the translated proteins. This RNA recoding is essential but when misregulated can lead to severe diseases.

A potential PhD project will aim at understanding the regulation and crosstalk between RNA modifications with the aim to understand modification patterns of relevance to immune recognition. This work will involve manipulation of RNA modifications, bioinformatic analysis, as well as tests for the immunogenicity of RNAs.

An alternative project aims at understanding the physiological consequences of an RNA recoding event in Filamins that affects cellular migration, vascularization of tumors, but also tissue stiffness. Here we aim at understanding the molecular and physiological consequences of an amino acid exchange in Filamins. This project will involve work with transgenic mice, physiological analyses of tissues, but also the therapeutic manipulation of RNA-modifications.

Skills and Expertise Gained Through the PhD

You will be trained in analyzing and understanding transcriptional dynamics and the impact of RNA modifications on cellular and organismic processes. You will learn how to analyze and manipulate RNAs with state-of-the-art sequencing and bioinformatic analyses. You will also be introduced to the use of transgenic animals to study physiological consequences of genetic alterations. Lastly, the project will introduce you to therapeutic strategies aimed at manipulating RNA modifications.

You will be embedded in the RNA-Biology program at MedUni where you will participate in weekly seminars on RNA-related topics, attend an annual RNA symposium, and have acces to bioinformatic help. Further, you will be in contact with several labs working on related topics, allowing state-of-the-art education.

A common roadblock in AI based glioma research and a cross-cutting issue in many AI and healthcare developments is the small sample size. The aim of this project is to develop strategies for the curse of dimensionality often faced in machine learning (ML) applied to medical imaging, i.e. having feature-rich data but a small amount of training data. In this project different strategies will be developed to increase the quantity of training data, e.g. diagnostic and follow up MRI, dynamic FET-PET/MR data, PET/CT scans as well as preclinical imaging data of glioma. Techniques will include image transformations, the generation of synthetic images with different architectures of General Adversarial Networks (GANs) and stable-diffusion based methods using a variety of on-site as well as publicly available datasets. A goal is also to have a better understanding of the requirements for the quality and quantity of synthetic medical imaging data in order to be beneficial for deep learning in glioma imaging. We will also gain insights about domain adaptation between different medical imaging tasks (e.g. MR <-> PET, preclinical <-> clinical data). Finally, we will define best practice data+ML-workflows for glioma data, depending on imaging modality and clinical questions.

Embedment of the PhD Project

This PhD position is part of the CONGLIOMERATE project, which aims to improve glioma diagnosis by integrating histopathology, multiparametric MRI, PET, and clinical data using AI methodologies. The project is one of five connected PhD positions, supervised by experts in medical imaging, AI, and clinical research from the Medical University of Vienna and the University of Applied Sciences Technikum Wien.

The selected candidate will work within an interdisciplinary and international research team, with opportunities for collaboration through regular retreats, joint courses, and team-building activities. Additional training includes clinical observation days, presentations at international conferences, and funding for research visits to other institutions. This specific project will be carried out at the Competence Center for Artificial Intelligence & Data Analytics (AIDA) of the University of Applied Sciences Technikum Wien.

Skills and Expertise Gained Through the PhD

• Medical Imaging Expertise – In-depth knowledge of PET and MRI technology, including image acquisition, reconstruction, and analysis.
• Nuclear Medicine and Tumor Biology – Understanding of tracer pharmacokinetics, radiopharmaceuticals, and their applications in oncology and other medical fields.
• AI and Data Analysis for Imaging – Experience with machine learning, deep learning, and other AI techniques applied to medical image processing and data interpretation.
• Interdisciplinary and Intercultural Communication – Experience working in multidisciplinary teams with experts from medicine, physics, engineering, and computer science, often in an international research environment, requiring strong scientific and technical communication skills in multiple languages.
• Research Methodology and Data Interpretation – Experience in designing and conducting experiments, handling large datasets, and drawing meaningful conclusions from research findings.
• Project Management and Problem-Solving – Skills in managing long-term projects, developing research strategies, and addressing complex technical challenges.
• Scientific Communication and Writing – Ability to present research findings clearly through scientific publications, conference presentations, and technical reports.

This combination of technical expertise, analytical skills, and strong communication abilities prepares PhD graduates for careers in academia, industry, and healthcare innovation.

One of the most pressing challenges in the follow up of glioma patients after therapy is pseudoprogression which mimics true progression in the imaging of glioma with their differentiation having a great impact on the further disease management. This project aims to develop an Machine Learning (ML)-based framework to differentiate true progression from pseudoprogression in treatment response monitoring using multi-modality imaging data, i.e. dynamic FET-PET and MR as well as data from the initial diagnostic assessment. The database consists of simultaneously acquired dynamic [18F]FET PET/MR data, separately acquired MR and PET/CT data from follow up scans, plus data from the initial diagnostic assessment as well as publicly available datasets. Multi-modality data preparation will include developing workflows for auto-registration, tumor segmentation and feature extraction algorithms. The ML-framework shall identify the underlying subvisual tumor fingerprint encoded in the functional imaging information and use attention-based fusion modules to evaluate performance alongside interpretability. The goal is to provide a clinically relevant ML framework to differentiate pseudo progression from true progression embedded in an automated workflow to reduce time burden in preparing multi-modality data for ML tasks. 

Embedment of the PhD Project

This PhD position is part of the CONGLIOMERATE project, which aims to improve glioma diagnosis by integrating histopathology, multiparametric MRI, PET, and clinical data using AI methodologies. The project is one of five connected PhD positions, supervised by experts in medical imaging, AI, and clinical research from the Medical University of Vienna and the University of Applied Sciences Technikum Wien.

The selected candidate will work within an interdisciplinary and international research team, with opportunities for collaboration through regular retreats, joint courses, and team-building activities. Additional training includes clinical observation days, presentations at international conferences, and funding for research visits to other institutions. This specific project will be carried out at the Competence Center for Artificial Intelligence & Data Analytics (AIDA) of the University of Applied Sciences Technikum Wien.

Skills and Expertise Gained Through the PhD

• Medical Imaging Expertise – In-depth knowledge of PET and MRI technology, including image acquisition, reconstruction, and analysis.
• Nuclear Medicine and Tumor Biology – Understanding of tracer pharmacokinetics, radiopharmaceuticals, and their applications in oncology and other medical fields.
• AI and Data Analysis for Imaging – Experience with machine learning, deep learning, and other AI techniques applied to medical image processing and data interpretation.
• Interdisciplinary and Intercultural Communication – Experience working in multidisciplinary teams with experts from medicine, physics, engineering, and computer science, often in an international research environment, requiring strong scientific and technical communication skills.
• Research Methodology and Data Interpretation – Experience in designing and conducting experiments, handling large datasets, and drawing meaningful conclusions from research findings.
• Project Management and Problem-Solving – Skills in managing long-term projects, developing research strategies, and addressing complex technical challenges.
• Scientific Communication and Writing – Ability to present research findings clearly through scientific publications, conference presentations, and technical reports.

This combination of technical expertise, analytical skills, and strong communication abilities prepares PhD graduates for careers in academia, industry, and healthcare innovation.

Adult-type diffuse glioma comprises multiple tumor types sharing diffuse infiltrative growth with destruction of large parts of the brain, and poor patient outcome. Their diagnosis and prognosis is based on the systematic integration of magnetic resonance imaging,  histological imaging features and molecular markers including DNA sequence, structural alterations and methylation patterns.

In this PhD project you will  develop novel machine learning methods (specifically representation learning methods) to link these modalities for individual diagnosis, and prognosis. We want to understand how they together predict the future of individual patients, and what their associations can tell us about the molecular mechanisms, that shape the phenotype, its change over time, and the possible response to treatment.

Techniques you will learn: As part of the PhD you will learn to develop novel cutting-edge methods in the area of machine learning, image- and molecular data analysis. You will become proficient in working in interdisciplinary teams, and understand state of the art approaches in sequencing- and molecular- data analysis, and medical image analysis.

You will  be part of a network: You will be member of the CIR Lab, and the newly founded Comprehensive Center of AI in Medicine (https://caim.meduniwien.ac.at). This will enable you to interact with a vibrant community of ML researchers across different medical fields. Your position is funded by the Austrian Science Fund (FWF) as part of a multi-disciplinary FWF funded doc.fund.connect program, and you will join together with 4 other students, enabling close interaction and joint work during your PhD studies. 

Glioma encompasses a wide range of brain tumors with different levels of aggressiveness. MRI is commonly used for glioma diagnosis but has limitations, particularly for low-grade gliomas and recurrence prediction. FET PET with the amino acid tracer 18F-fluoroethyltyrosine (FET) provides functional imaging to address these limitations. This project aims to develop an automatic pixel-wise kinetic modeling tool for dynamic FET PET to improve the diagnosis and follow-up of gliomas. To achieve this, the candidate will develope an AI-assisted tool that predicts personalized input functions based on factors such as age, gender, and size. Further, the candidate will develop an automatic processing pipeline for pixel wise kinetic modelling of dynamic FET PET/MRI data. The tool’s performance will be assessed and validated using existing PET/MRI and PET/CT datasets, along with histopathological data. Further, the project will explore the relationships between kinetic parameters and tumor characteristics observable through MRI. The final goal is to develope new evaluation tools for assessing glioma and improving the understanding of how FET uptake correlates with MRI-based tumor assessments, ultimately enhancing glioma diagnosis and monitoring.

Embedment of the PhD project

This PhD position is part of the CONGLIOMERATE project, which aims to improve glioma diagnosis by integrating histopathology, multiparametric MRI, PET, and clinical data using AI methodologies. The  PhD project is one of five connected PhD positions, supervised by experts in medical imaging, AI, and clinical research from the Medical University of Vienna and the University of Applied Sciences Technikum Wien.

The selected candidate will work within an interdisciplinary and international research team, with opportunities for collaboration through regular retreats, joint courses, and team-building activities. Additional training includes clinical observation days, presentations at international conferences, and funding for research visits to other institutions. This specific project will be carried out at the Center for Medical Physics and Biomedical Engineering, Medical University of Vienna.

Skills and Expertise Gained Through the PhD

• Medical Imaging Expertise – In-depth knowledge of PET and MRI technology, including image acquisition, reconstruction, and analysis.
• Nuclear Medicine and Tumor Biology – Understanding of tracer pharmacokinetics, radiopharmaceuticals, and their applications in oncology and other medical fields.
• AI and Data Analysis for Imaging – Experience with machine learning, deep learning, and other AI techniques applied to medical image processing and data interpretation.
• Interdisciplinary and Intercultural Communication – Experience working in multidisciplinary teams with experts from medicine, physics, engineering, and computer science, often in an international research environment, requiring strong scientific and technical communication skills.
• Research Methodology and Data Interpretation – Experience in designing and conducting experiments, handling large datasets, and drawing meaningful conclusions from research findings.
• Project Management and Problem-Solving – Skills in managing long-term projects, developing research strategies, and addressing complex technical challenges.
• Scientific Communication and Writing – Ability to present research findings clearly through scientific publications, conference presentations, and technical reports.

This combination of technical expertise, analytical skills, and strong communication abilities prepares PhD graduates for careers in academia, industry, and healthcare innovation.

Oral squamous cell carcinoma (OSCC) is associated with high morbidity and mortality, yet options for targeted therapies are scarce and of limited effectiveness. Polo-like kinase 1 (PLK1) is an oncogene with high potential as a drug target, and consistently overexpressed in OSCC. Nevertheless, clinical translation of PLK1 inhibition has yet to be achieved in this cancer type. In the PhD project offered here, understudied aspects of the biology of PLK1 in OSCC, including its role in the regulation of the primary cilium (a cell-cycle related structure) and of chromosome instability, shall be explored. Moreover, to reveal novel approaches towards the therapeutic exploitation of PLK1 overexpression in OSCC, drugs synergizing with PLK1 inhibition, as well as vulnerabilities specifically associated with PLK1 overexpression, shall be identified through comprehensive genetic and pharmacological screens. Results obtained with human OSCC cell lines will be corroborated in primary patient samples and in a congenic, transplantation-based mouse model of OSCC.

Skills you will obtain during your PhD:

A wide range of state-of-the art molecular and cell biology methods will be used, so that any candidate can expect to acquire novel skills during her/his PhD work in this project. Moreover, a congenic, orthotopic mouse model will be employed, offering an additional opportunity to gain novel expertise even for a candidate who has worked with mice before. The selected candidate will have the opportunity to attend practical training courses (e.g., flow cytometry, FELASA). Regular interactions with the supervisor and practical training by a postdoc working in the same field will ensure that problems can be spotted and addressed in due time and work progresses efficiently.

Project coolaborators: 

The position is funded through a bilateral grant that was acquired together with two Czech research groups (led by Matouš Hrdinka, Ostrava, and Marcela Buchtová, Brno). These will contribute their expertise on OSCC and on primary cilia to the project. Exchange will be fostered through regular online and in-person meetings and the possibility for mutual research visits of a few weeks or months.

We are a translational bioinformatics research group at the Department of Dermatology at the Medical University of Vienna. Our research approach merges the analysis of primary patient samples with the integration of large-scale, public ‘omics datasets. For this, we are able to work on Austria’s largest supercomputer to perform these analyses.

In this project, we investigate the role of B cells in the context of inflammatory skin diseases. Our group was among the first to show that B cells are vital to the anti-tumour immunity in human melanoma. Additionally, we just completed the largest single-cell RNA-seq analysis of primary cutaneous B cell lymphoma. Thereby, we were able to create a comprehensive, single-cell based atlas of B cell phenotypes in the skin.

Based on this data, we will now investigate the role of B cells in inflammatory skin diseases. We will combine data from primary patient samples from our outpatient clinics with the large-scale re-analysis of public scRNA-seq data.

As part of this and other ongoing bioinformatics projects, we have ongoing collaborations with the Karolinska Institute (Maria Kasper - coordinator of the skin human single-cell atlas project) and the EMBL-European Bioinformatics Institute (Cambridge, UK) with whom we develop pathway algorithms for multi-omics data analysis.

As part of this project, you will receive training in R and Python-based bioinformatic analyses of (large-scale) ‘omics data and learn how to perform automated analyses on super-computer environments using nextflow and docker containers. You will also receive training in immunology, with a focus on inflammatory skin diseases.

We offer

• A cutting-edge research environment at the intersection of bioinformatics and translational dermatology.

• The opportunity to work on Austria’s largest supercomputer, handling large-scale omics datasets.

• Hands-on training in R and Python for bioinformatics, including nextflow and Docker for automated analysis.

• Access to unique primary patient sample data and large-scale public scRNA-seq datasets.

• A collaborative, interdisciplinary team with strong international partnerships, including the Karolinska Institute and EMBL-EBI (Cambridge, UK).

• Training in immunology and inflammatory skin diseases, linking computational analysis with clinical research.

• A dynamic and supportive environment within the Department of Dermatology at the Medical University of Vienna.

Expertise and skills gained during the PhD

• Training in R and Python-based bioinformatic data analysis
• Training in large-scale multi-omics data integration
• Automated data analysis using nextflow and docker for reproducible research
• Use of high-performance computing environments
• Depending on interest: Training in sample prep for transcriptomics and proteomics

The AI Institute / Center for Medical Data Science is recruiting an ambitious PhD student who wants to pursue a scientific career in the domain of Machine/Deep Learning for Medical Imaging with applications in eye care. The position is on automated detection and profiling of retinal disease from longitudinal multimodal imaging as part of an exciting new ERC project HealthAEye: Health-monitoring with AI-enabled smartphone-based imaging of the eye.

Research topic will span the Deep Learning areas of Foundation Models, Image Domain Adaptation, and Uncertainty Quantification. The candidate will be immersed in a multidisciplinary environment working closely with a team of AI experts, software engineers, and medical doctors in the fascinating and interdisciplinary field of AI in Retina. The output will have a real-world impact on healthcare by enabling a groundbreaking approach to home-based monitoring of patients suffering from retinal diseases, leading causes of blindness today.

The lab is embedded in a vibrant network of a rapidly growing cluster of AI researchers dedicated to addressing the challenges of AI in medicine, and benefit from the methodological expertise in machine/deep learning at the AI Institute. Throughout their PhD journey, the student will acquire in-depth knowledge and hands-on experience in advanced deep learning methodologies and statistical modelling. They will develop the skills to apply these techniques to complex and high-dimensional medical imaging datasets, gaining expertise in designing, implementing, optimizing and evaluating machine learning models for healthcare.

We offer

• Opportunity to work and do cutting-edge research in deep learning for medicine and healthcare.

• Immersion into an interdisciplinary and international research environment, and a multi-cultural lab.

• Access to large multi-modal, curated, and annotated medical imaging datasets.

• Access to a dedicated high-performance computing (HPC) cluster containing the latest generation GPUs.

• Collaboration with several renowned academic institutions, as well as partnership with imaging device companies.

The Department of Epidemiology is conducting a project in which the health consequences of a disturbed “internal clock” will be researched in depth. Eva Schernhammer is building on earlier research on this topic and taking new steps towards personalized medicine. By using epidemiological measurement methods to investigate the interrelated mechanisms of the circadian rhythm, it should be possible to assess the personal risk of negative effects of an out-of-sync “internal clock”. This is particularly relevant for the 20 percent of people in employment worldwide who are unable to maintain a regular day-night rhythm due to shift work and changing working hours. Repeated exposure to light at night in particular is associated with disorders of the circadian system and subsequently with an increased risk of serious chronic diseases and premature mortality. However, not everyone falls ill in the same way: Each individual's risk is determined by genetics and environmental influences.

The ERC Advanced Project “CLOCKrisk” aims to further investigate the adverse health consequences of a disturbed circadian clock, with a step in this direction of estimating individual risk (“personalized medicine”). Applied approaches include big data analytics, omics and other biomarker analyses using standard statistical approaches and working with an extensive network of international collaborators.

Skills and Techniques to be Acquired During the PhD Training:

The PhD candidate will gain expertise in advanced epidemiological methods, big data analyses, and cutting-edge techniques related to personalized medicine. The training will provide the candidate with hands-on experience in omics technologies and biomarker analysis, using state-of-the-art statistical approaches. The candidate will have the opportunity to collaborate internationally with experts in the field, and there may be possibilities for short-term stays at Harvard University to further enhance their research experience. This position offers an exciting chance to contribute to a globally relevant research project while developing a broad skill set in epidemiology and data science.

Metastasis is the leading cause of cancer related deaths. Specific cancers have a prevalence to metastasize to specific organs. The underlying mechanisms for this organotropism are poorly understood. In this project you will investigate how tumor and metastatic cells and their immune-microenvironment are different in different host organs and how malignant cells remodel distant tissue niches. We are focusing on finding ways to empower the immune system to combat metastasis using cutting-edge single-cell and spatial omics technologies. We use clinically highly relevant disease models of metastasis and patient samples to investigate the complex interplays of tumor and immune cells in metastasis. The use of modern omics technologies makes it possible to discover new tumor immune cell interactions in metastasis that have not been described before. By studying these interactions between metastatic cancer cells and immune cells, we hope to uncover new insights that could lead to improved treatments for cancer patients.

Techniques you will learn: Our interdisciplinary approach combines different expertise to tackle the complex interplay of tumor and immune cells in metastasis including experimental and computational biology, as well as translational research. As a member of our team, you will receive training in both experimental and computational biology that are equally important skill sets for modern cancer researchers. You will be trained to generate and analyze single-cell and spatial omics data using the latest technologies, learn how to generate scientific hypothesis and to validate your hypothesis in appropriate advanced in vitro organoid and in vivo metastasis models systems. You will have the opportunity to present your work at conferences and collaborate with researchers from around the world, helping you grow professionally and make valuable connections in the field.

You will be part of a network: And as part of the unique SHIELD PhD program, you will have access to coordinated courses and workshops and a network of experts in immunology and cancer, providing even more opportunities for learning and collaboration.

The COVID-19 pandemic has once again impressively demonstrated the vulnerability of human societies to infectious disease. To prepare for future emergent disease scenarios the newly founded Ludwig Boltzmann Institute for Science Outreach and Pandemic Preparedness (LBI-SOAP) pursues an integral approach to strengthen the scientific literacy of the public through transdisciplinary science methods and improve the understanding of circulating pathogens in an urban landscape.

We invite applications for a PhD position in the field of environmental pathogen surveillance, with a particular focus on pathogens with zoonotic potential.

If you feel attracted by the prospect to develop and apply innovative techniques to detect and sequence virus from environmental samples, if you have a background in molecular biology or bioinformatics, and interest to learn the other, if you consider yourself having a tinkerer's mindset, we would be excited to get to know you.

As a PhD candidate, you will be working at the intersection of virology, epidemiology and One Health. You will have the opportunity to participate in transdisciplinary projects working jointly with divers communities and contribute to make our society more resilient to future pandemic threats.

The LBI-SOAP at the Medical University of Vienna offers an exceptional and applied research environment, embedded in an extended network of infectious diseases and public health experts. You will join a vibrant team of researchers dedicated to address future health challenges through innovative science.

Skills gained during the course of the project:

During the PhD project you will develop and apply techniques to collect environmental samples, process these to concentrate viruses, detect viruses with PCR based methods, sequence positive samples (from library prep to data analysis), and contextualize the obtained information with phylogenomics methods.

The gut microbiome is crucially important for human health. It is capable to produce anti-inflammatory short-chain fatty acids (SCFAs) that balance our immune responses at systemic level. In this project, we aim to uncover the pillars of high-performing SCFA producer microbiota, model both natural and synthetic gut communities, and – with help of these models - engineer even more effective microbial functions. We will use machine learning and network science to identify the most efficient consortia and metabolic community models to identify SCFA production bottlenecks.

Join us for a fully funded computational PhD position to investigate how to boost the health benefits of our gut microbiota.

We expect that this combination of microbiota modeling and engineering will open new avenues for treatment and prevention of inflammatory diseases and will teach us how to control and manage bacteria to improve human health.

Techniques you will learn: During this PhD project, you will learn cutting-edge modeling techniques, including novel ML methods, network inference, metabolic modeling and multi-omics data analysis to dissect microbiome community data. You will be part of scientific topic of high clinical and societal importance.  

Embedment of your PhD project

The PhD project is part of the EnhanceFun project funded by the WWTF. You'll conduct computational modeling at the Medical University of Vienna, and partner up with a second PhD candidate covering the experimental side of the study (University of Vienna, David Berry lab). You will be member of the Comprehensive Center of AI in Medicine and a vibrant ML student network. The lab is part of the interdisciplinary Research Division Infection Biology and the clinical Department of Medicine I of the Medical University of Vienna, located in one of the most livable cities worldwide. You will be embedded in an international team of like- and open-minded colleagues with a great passion for science and microbiome research.

The aim of this study is to assess treatment-specific responses on tumor characteristics using multimodal PET/MR imaging in preclinical glioma models. This research seeks to enhance our understanding of the interrelationships between the biological background of glioma and the imaging data obtained through MRI and PET, bridging clinical, pre-clinical, and basic research domains. To achieve this, the candidate will first evaluate [18F]FET uptake kinetics in glioma cells in vitro, both before and after therapy. Next, we will generate orthotopic glioma models by injecting glioma cells into the brains of male and female mice. Simultaneously, we will apply [18F]FET-PET with multiparametric MRI to study tumor anatomy, microvasculature, cellularity, hypoxia, and metabolism. [18F]FET-PET images will be analyzed by the candidate using kinetic modeling based on insights from human data analysis. The candidate will explore the relationships between MR parameters and [18F]FET-PET metrics, focusing on aspects such as changes in the microvasculature and the [18F]FET influx rate constant. Subsequently, mice will be treated with anti-cancer drugs and the candidate will evaluate treatment-specific responses. At the end of the study histology images will be matched with PET/MR images to provide a comprehensive understanding of the tumor. Ultimately, this study aims to provide new insights into complex tumor characteristics and their interplay through simultaneous PET/MRI. 

Embedment of the PhD Project

This PhD position is part of the CONGLIOMERATE project, which aims to improve glioma diagnosis by integrating histopathology, multiparametric MRI, PET, and clinical data using AI methodologies. The project is one of five connected PhD positions, supervised by experts in medical imaging, AI, and clinical research from the Medical University of Vienna and the University of Applied Sciences Technikum Wien.

The selected candidate will work within an interdisciplinary and international research team, with opportunities for collaboration through regular retreats, joint courses, and team-building activities. Additional training includes clinical observation days, presentations at international conferences, and funding for research visits to other institutions. This specific project will be carried out at the Center for Medical Physics and Biomedical Engineering, Medical University of Vienna.

Skills and Expertise Gained Through the PhD

• Medical Imaging Expertise – Advanced knowledge of PET and MRI technology, including imaging principles, acquisition, reconstruction, and analysis.
• Preclinical Imaging and Tumor Biology – Understanding of preclinical imaging procedures, disease modeling, and tumor biology in oncology research.
• Interdisciplinary and Intercultural Collaboration – Experience working in multidisciplinary teams across medicine, physics, engineering, and AI, often in an international research environment, requiring strong communication skills.
• Research Methodology and Data Interpretation – Expertise in experimental design, data acquisition, statistical analysis, and interpretation of research findings.
• Project Management and Problem-Solving – Ability to plan and execute long-term research projects, develop innovative solutions, and address complex technical challenges.
• Scientific Communication and Writing – Skills in publishing research, presenting findings at international conferences, and writing technical reports.

This combination of specialized knowledge, analytical skills, and interdisciplinary experience prepares PhD graduates for careers in academic research, industry, and healthcare innovation.