University: TUD
Faculty: Electrical Engineering, Mathematics and Computer Science
Integrated data science with focus on health
Applied Mathematics department is seeking tenure track candidates to strengthen the department's main theme Health & Wellbeing. Within CENTRE project, a crucial part in developing risk profiles is to combine different and disparate data sources, acquired from personal information, historical data, expert judgment and sensors. State-of-the art methods are developed for similar data types. Thus, novel methodologies are required to integrate these different data sources and model them jointly in order to detect and predict events of interest in life-style changes or deterioration, and therefore profile risks in individuals.
The TT is expected to collaborate with the other researchers at EEMCS focusing on data science and to strengthen the department's and faculty's national and international position in the field. The TT position will be accompanied by one PhD project, co-supervised with the co-applicants from TUD and UT. The TT candidate is expected to continue focusing on health-related research beyond the duration of the RECENTRE project.
University: TU/e
Faculty: Electrical Engineering
THz and Photonic health sensors
The Technical University of Eindhoven is targeting the advancement of sensing technology and will be engaged in the deep-tech exploration of emerging sensing solution using THz and optical waves. The tenure-track scientist will be recruited to integrate the knowhow of the complementary TT researchers in the other universities to define, design and demonstrate the use of emerging sensing technologies to answer the requirements emerging from the joint research. The candidate is expected to have a proven track record in the integration and co-integration of semiconductor technologies and to have a good understanding of the health-related sensing challenges. The candidate will be accompanied by a PhD student and two PhD projects co-supervised with researchers from UT and WUR that will offer the needed expertise in the definition of the requirements and deployment strategies of new sensors. It will also be part of the activity to build and mine the emerging data sets from the sensory data.
University: UT-1
Faculty: Electrical Engineering, Mathematics and Computer Science
Dynamic modelling for early detection of disease over time
By combining different data sources, you will come to holistic patient data to develop and compare prediction models to detect new complaints or deterioration in health over time. For this, you will make use of techniques from data science, AI and dynamic systems theory such as critical thresholds or tipping point analysis. This will enable prediction and detection of clinically relevant changes in an early stage, which will assist in clinical decision making and enable early treatment, resulting in a higher quality of life and lower healthcare costs. The models will be applied to cancer and obesity, such that patient can be monitored in their homes instead of primary/secondary care. Monitoring data will enable updating the models and enhancing the individual predictions. A PhD will be attracted to collaborate on dynamic modelling of late effects. Additionally, you will collaborate closely with other researchers from the UT, as well as WUR, TUD and TU/e.
University: UT-2
Faculty: Behavioural, Management and Social Sciences
High engagement of at-risk populations with unobtrusive high-tech monitoring and intervening in daily-life by using experimental adaptive future-self approaches
Development of eHealth monitoring and coaching systems starts with including participants needs and wishes to determine the scope and acceptance of technology. This "peoples wishes first approach” is an important step in developing meaningful and effective health technology. The downside is that users can only express needs which they are currently able to imagine. Building on these needs and wishes, you will investigate ways to proactively engage participants (cancer and obesity) with innovative, non-obtrusive monitoring and tailored intervention technology by let them experiment with sensor related future-self perceptions and the role of the sensor system for future daily living in an adaptive virtual environment including digital twins. Together with a PhD student, you will identify preferences in a more vivid and valid way and develop systems that contribute optimally to meaningful behavior change of high-risk populations to ensure successful and sustainable implementation of the system.
University: WUR-1
Faculty: Agrotechnology and Food Sciences
Measuring the unmeasurable: identifying how disease status impacts health of multiple organ systems over the life course.
Individuals living with lifestyle-related diseases are at increased risk of developing (co)morbidities. However, it is not known yet how exactly these diseases impact health of different organ systems on the long-term, such as muscle quality, bone mineral density, hormone levels, blood pressure, liver, and even fetal growth and development. In collaboration with other technical universities, you will first investigate how lifestyle and health of different organ systems can best be home-monitored in high-risk patients. Second, together with other 4TU partners you will study how lifestyle and health of these individuals can be improved by means of targeted, individualized lifestyle interventions while using a defined set of home-sensors. The envisaged PhD-student will define which monitoring devices are most suitable to simultaneously assess health of different organ systems over time and design a human intervention study to test the effectiveness of targeted lifestyle interventions by using a multi-organ system approach.
University: WUR-2
Faculty: Social Sciences Group
Adaptive lifestyle interventions targeting behavior change of patients with different holistic profiles
Many health issues, such as obesity and cancer, are highly complex (i.e., dynamic, multicausal, and manifest idiosyncratically). This calls for behavior change interventions that are individualized, contextualized and timely, as a one-size fits all approach does not match the complexity. You will investigate how to move from holistic patient profiles to recommendations for adaptive lifestyle interventions, considering the processes driving behavior change, engagement, costs, effectiveness, scalability and feasibility. You will use optimization trials (e.g., SMART design via the MOST research framework) to devise decision rules that specify whether, how, and when to alter the intensity, type, or delivery of treatment at critical clinical decisions during the life course, making use of the continuously collected sensor data from the patients. A PhD will be attracted to collaborate on testing the optimized adaptive interventions using innovative research designs, such as micro-randomized trials and n-of-1 trials.
