Stavroula Mougiakakou

Professor at University of Bern

University of Bern
Country flagSwitzerland

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About

Stavroula Mougiakakou is a Professor at the University of Bern, Switzerland. Her research primarily focuses on the applications of artificial intelligence in healthcare, particularly in assessing nutrition and dietary habits through mobile applications. She has published extensively on topics such as the evaluation of AI-powered clinician tools and the impact of automated nutrition assessment systems on patient care.

Positions (1)

Publisher
source

Stavroula Mougiakakou

University Name
.

University of Bern

The ARTORG Center for Biomedical Engineering Research at the University of Bern is a leading transdisciplinary center dedicated to advancing medical technology research. The Artificial Intelligence in Health and Nutrition (AIHN) laboratory within ARTORG is offering a fully funded PhD position focused on mechanism-informed multimodal AI models. This opportunity is ideal for candidates interested in developing next-generation AI methods that integrate mechanistic knowledge with data-driven approaches, contributing to a large international research consortium. The successful candidate will engage in interdisciplinary research, collaborating with engineers, computer scientists, healthcare professionals, and industrial partners. Key research topics include the development of mechanism-informed multimodal AI models, mechanism formalisation and discovery using knowledge-driven, data-driven, and hybrid approaches, and causal inference and modelling (including structural, temporal, and dynamic causal models). The project aims to address unmet clinical needs and envision future challenges in diagnosis, monitoring, and treatment, ultimately creating innovative healthcare technology solutions. Applicants should possess an MSc in computer science, engineering, applied mathematics, or a related field. Essential qualifications include experience with causal inference methods (such as SCMs and Bayesian networks), knowledge of dynamical systems or systems biology, experience with multimodal biomedical data, familiarity with knowledge graphs and ontologies, and proficiency with generative AI models (including VAEs, diffusion models, and multimodal transformers). A strong background in machine learning, mathematical modelling, probability, and optimization, as well as experience in Python and deep learning frameworks (e.g., PyTorch), is required. Excellent command of English and a commitment to multidisciplinary research are also expected. The position is funded for up to four years, with salary provided according to Swiss University regulations. The University of Bern offers an international environment, excellent research opportunities, and promotes healthy work-life balance and safe working conditions. It is an equal opportunity employer and strives to increase the number of women at all levels in its faculties. To apply, candidates should send their application to Prof. Stavroula Mougiakakou ([email protected]). Applications must include a motivation letter, detailed curriculum vitae, abstract of the master thesis, and contact details of two referees, all combined into a single PDF file. The expected start date is July 2026, and applications will be reviewed until the position is filled. For further information about the AIHN laboratory and the ARTORG Center, visit the lab website . The position is part of a larger international consortium, offering close collaboration and exposure to cutting-edge research in biomedical engineering and AI.

4 months ago

Articles (6)

Year:

2025

Diabetes mellitus (DM) and obesity are chronic medical conditions associated with significant morbidity and mortality. Accurate macronutrient and energy estimation could be beneficial in attempts to manage DM and obesity, leading to improved glycemic control and weight reduction, respectively. Existing dietary assessment methods are subject to major errors in measurement, are time consuming, are costly, and do not provide real-time feedback. The increasing adoption of smartphones and artificial intelligence, along with the advances in algorithms and hardware, allowed the development of technologies executed in smartphones that use food/beverage multimedia data as an input, and output information about the nutrient content in almost real time. Scope of this review was to explore the various image-based and video-based systems designed for dietary assessment. We identified 22 different systems and divided these into three categories on the basis of their setting for evaluation: laboratory (12), preclinical (7), and clinical (3). The major findings of the review are that there is still a number of open research questions and technical challenges to be addressed and end users—including health care professionals and patients—need to be involved in the design and development of such innovative solutions. Last, there is a clear need that these systems should be validated under unconstrained real-life conditions and that they should be compared with conventional methods for dietary assessment.

Year:

2022

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2021

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2021

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2020

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2020

Collaborators (2)

Stergios Christodoulidis

Assistant Professor

CentraleSupélec

FRANCE
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Arzu Coltekin

Prof. Dr. / PI

University of Applied Sciences and Arts Northwestern Switzerland

SWITZERLAND
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