Description of the Scientific Environment
The Quality of Life Technologies (QoL) lab at the Geneva School of Economics and Management (GSEM) and the Center for Informatics (CUI) of the University of Geneva is looking for a motivated junior scientist to join the lab (salary level: assistant A2, 75–100%). The lab’s research focuses on fundamental and algorithmic problems, as well as human-centric challenges of the systems that enable the assessment and improvement of human behaviour, health, and quality of life in the long term.
Specifically, the topic concerns research on quality-of-life assessment, modeling, and its improvement for patient populations while leveraging current QoL Lab expertise in mobile computing for data acquisition and expertise in machine learning, including deep learning for data modeling. The research will employ user-centered design methods, mixed-methods design (quantitative and qualitative, in longitudinal, large-scale data collection studies with patients) and rely on interdisciplinary collaboration for data collection and the evaluation of the resulting models.
Research Context
Chronic and long-term conditions, including non-communicable diseases (NCDs) such as cardiovascular disease, diabetes, chronic respiratory disease, cancer-related trajectories, and neurological conditions, have a profound impact on global health. These conditions have multiple and interacting causes, including behavioral risk factors such as physical inactivity, unhealthy diet, smoking, alcohol consumption, insufficient sleep, and poor stress management, which may substantially influence disease onset, progression, treatment response, and long-term health outcomes. Also, many chronic and long-term conditions are also increasingly diagnosed, treated, and monitored across longer periods of the life course. There is an urgent need to address the challenges posed by chronic and long-term conditions, not only from a healthcare perspective but also in terms of their broader societal and economic implications.
Quality-of-life and health-related quality-of-life outcomes are increasingly recognized as important outcomes in chronic and long-term conditions, as symptoms, treatment burden, rehabilitation needs, and long-term monitoring can substantially affect daily life, including patients’ ability to reconcile personal, professional, and social activities with health status and care demands. Currently, patients’ symptoms, functioning, psychological status, and health-related quality of life are commonly assessed using Patient-Reported Outcomes (PROs) and related patient-reported outcome measures. However, these assessments may be affected by reporting biases, ceiling and floor effects, recall burden, and limited sensitivity to change at the extremes of their scales. In parallel, smartphones and wearable devices increasingly enable the continuous measurement of sensor-derived behavioral and functional indicators over short and long time scales. An important scientific question is whether such sensor-derived digital measures can provide complementary, clinically meaningful information about health, functioning, and quality of life.
The main scientific focus of this work is the development of core outcome digital measures of health that contribute to quality of life: from qualitative methods identifying meaningful aspects of health, through sensor selection and data acquisition, to analytical validation, clinical validation, and progression toward candidate digital endpoints. To this end, the project will investigate the operational and human factors influencing the use of wearables, smartphone applications, PROs, and HRQoL instruments to collect clinically meaningful longitudinal datasets in clinical trials, research studies, and routine clinical practice.
The candidate will contribute to the European Organisation for Research and Treatment of Cancer (EORTC) EMBED Research Project and is expected to lead the Swiss consortium activities under supervision. The candidate will be mentored to lead a prospective longitudinal data collection study with a cohort of Swiss patients from the Breast Cancer Center of the University of Geneva Hospitals (HUG). The study will use mixed methods, including baseline, interim, and exit interviews, surveys, focus groups, passive wearable data collection (via Withings ScanWatch 2), smartphone-based data collection, and computational modeling (via the mQoL Living Lab). Qualitative methods will be used to identify which aspects of health, functioning, symptoms, treatment burden, and daily life are meaningful to patients and clinicians. The mixed-methods design will integrate qualitative insights, PRO/HRQoL data, sensor-derived longitudinal data, and clinical information to support the interpretation and validation of digital measures. Breast cancer is treated here as a concrete use case of a long-term health trajectory involving symptoms, treatment burden, survivorship, monitoring, and quality-of-life consequences. The EMBED project provides the clinical and methodological context for studying how digital measures can capture meaningful aspects of health and daily functioning in a long-term condition trajectory.
Using user-centered design methods and an iterative data acquisition and modeling approach, the project will examine data quality, feasibility, reliability, analytical validity, clinical validity, and the interpretability of the resulting digital measures. Particular attention will be paid to participant burden, adherence, acceptability, data completeness, and the usability of the technologies in real-world clinical and daily-life settings. The longer-term ambition is to identify which sensor-derived measures are sufficiently meaningful, reliable, analytically valid, and clinically valid to serve as candidate digital endpoints in clinical research. The scientific contribution lies in connecting patient- and clinician-defined meaningful health concepts with sensor-derived digital measures and their validation as potential endpoints.
The candidate is also expected to lead efforts in the acquisition and management of selected retrospective EHR datasets from HUG, which are expected to include only limited behavioral and quality-of-life-related outcomes.
The research conducted within this PhD project will contribute to software being continuously developed by QoL Lab members as part of the mQoL Living Lab infrastructure (including Android and iOS platforms, Flutter-based mobile development, and programming in Python and Kotlin). The applicant should be comfortable using standard data science and machine learning libraries, such as pandas, scikit-learn, PyTorch, and TensorFlow/Keras and data visualization libraries, such as Plotly, Seaborn, and Matplotlib. Additionally, the CUI FacLab, equipped with 3D printers, laser cutters, and other prototyping tools, may be used when relevant for the development or adaptation of study-related technologies.
The principal QoL supervisor is Professor Katarzyna Wac, while the daily co-supervisor is MSc Alexander Horst. The clinical co-supervisors are Dre Anita Wolfer and other clinicians from the HUG’s Breast Cancer Center.
Main responsibilities include:
Applicants are encouraged to apply even if their expertise and interests only partially match these responsibilities.
The selected candidate will be hired on a 1-year renewable contract. The position has a maximum duration of 5 years. In the first year, there is a 3-month probationary period during which either party may terminate the employment agreement.
Formal Requirements
Applicants should hold an MSc degree in computer science, bioinformatics, human-computer interaction, or related fields with excellent results and professional proficiency in English and French. As criteria for assessing the qualifications, emphasis will also be placed on previous projects, publications, and relevant clinical, human-subjects research or industrial experience (if any).
Application Procedure
The application, in English, must be submitted electronically as one PDF file to Prof. Katarzyna Wac (katarzyna.wac@unige.ch) and MSc Alexander Horst (alexander.horst@unige.ch). Please include:
The University of Geneva is committed to creating an inclusive work environment with a diverse workforce. All qualified applicants will receive consideration for employment without regard to race, religion, gender, sexual orientation, national origin, disability, or age.
Questions
For specific information about the position, please contact Prof. Katarzyna Wac (katarzyna.wac@unige.ch) and MSc Alexander Horst (alexander.horst@unige.ch).
If you wish to meet us before you apply feel free to join our QoL Summer School starting on 29 June 2026 – see details on the WEB.