Knowledge Management team – IDLab (Ghent University, Belgium)
IDLab is a core research group of imec, a world-leading research and innovation hub in nanoelectronics and digital technologies, with research activities at Ghent University. IDLab performs fundamental and applied research on data science and internet technology, and is, with over 300 researchers, one of the larger research groups at imec. Our major research areas are machine learning and data mining; semantic intelligence; multimedia processing; distributed intelligence for IoT; cloud and big data infrastructures; wireless and fixed networking; electromagnetics, RF and high-speed circuits and systems.
The knowledge Management team is embedded in this stimulating environment. This team performs research into a) expressive semantic stream and distributed reasoning, b) the incorporation of expert knowledge in data analytics algorithms, c) hybrid AI, fusing semantic models and machine learning, and d) explainable AI by leveraging Knowledge Graphs. This research is mainly applied to the domains of predictive healthcare and industry 4.0 in order to realize context-aware and personalized decision support systems.
Chronic diseases consist of conditions that are prolonged in duration (>3 months), do not resolve spontaneously, are rarely cured completely and gradually become worse. Momentum is growing internationally on the urgency to act on these chronic diseases, which affect one-third of the European population aged 15 and over in Europe. Chronic diseases are a growing problem because of the aging of
the population and the number of deaths caused by those diseases is expected to rise dramatically in the next decades. Chronic diseases lead to the premature death of more than 550 000 people aged 25 to 64 each year across EU countries. 70% to 80% of all healthcare costs in the EU – an estimated €700 billion – are currently spent on chronic diseases.
Our lifestyle has a tremendous impact on our overall health. The combination of healthy lifestyle factors, e.g. maintaining a healthy weight, exercising regularly, following a healthy diet, reducing stress and not smoking, seem to be associated with as much as an 80% reduction in the risk of developing the most common and deadly chronic diseases. Our lifestyle and environmental factors are also heavily correlated with the on-set of symptoms of chronic diseases, e.g. migraine attacks. Moreover, treatment plans for chronic disease, e.g. cancer treatments, also seem to have greater effect in people with healthy lifestyles. As such, to allow for proper and fast diagnosis, follow-up, prevention and treatment of chronic diseases, it is of utmost important to get an accurate view on the daily life of the patients and follow them up closely.
Therefore, mobile health applications, e.g. fitness trackers, smart watches, wearable sensors and mobile applications, have become popular to monitor behavior and inform healthier lifestyle choices. They aim to empower patients by collecting vast amounts of behavioral and physiological data and visualizing it in a comprehensive manner. This enables patients to use these insights to make informed decisions on how to live healthier lives, but also to uncover correlations to their health status and thus improve their disease management. Due to their accessibility and ease of use, mobile health applications have a potential for high reach. With their ability to monitor general health continuously within and outside care facilities, wearables hold a significant potential to enhance management of chronic diseases.
However, the current mHealth apps only give limited insight to the patients, and the medical professional is often not included in the loop at all. Apps either work through questionnaires asked to patients to gain more insight in their lifestyle or they use sensors and wearables to collect physiological data. The latter applications however only visualize this sensor data to the patient and medical professionals in comprehensive graphs and figures, but offer no advanced data analytics on the data to give insight on how these data are correlated to possible diagnoses, occurrence of symptoms or health outcomes. The first applications sometimes use medical guidelines and static rules to translate the input of the patient about his/her profile towards possible risks for chronic diseases. However, limited insight can only be gained in the lifestyle of patients through standardized questionnaires, as such true personalized and actionable insight is lacking. They also give no insight to the patient on triggers for specific symptoms or lifestyle changes that would be beneficial for health outcomes.
Therefore, in this PhD, we wish to investigate novel AI techniques that are able to process the data collected through wearables, sensors and smartphones and combine them with the wealth of data already available about the patient (e.g. profile information, context data, questionnaire data) in order to uncover new insights and empower the patient. The goal is to give the patient and medical professionals personalized insight in how the lifestyle and patient profile are correlated to the occurrence of disease symptoms and health outcomes.
We offer a competitive salary with interesting social benefits and a challenging, stimulating and pleasant research environment, where you can contribute to worldwide research on AI in healthcare. During your research, the following activities will be part of your work:
- Analyze the current existing AI solutions for chronic disease management and design new AI algorithms and methodologies for realizing personalized and patient empowering chronic disease follow-up.
- Build-up hands-on experience by implementing the designed algorithms & evaluating them on data collected from patients.
- Thoroughly evaluate the designed algorithms, both through simulations as well as data collected from patients, both in test environment (e.g. the IDLab HomeLab) as well as in real life.
- Participate in European and national research projects, in collaboration with industry and healthcare professionals.
- Publish and present the research results at international conferences and in scientific journals.
- Work towards realized a PhD in about 4 years.
- Build towards a future research career (in academia or industry) through project experience and high-profile scientific publications or towards a promising industry career in AI & eHealth through collaborations with several high-impact industry partners.
We are looking for candidates with the following interests and skills:
- You have a Master’s degree in Computer Science, Engineering, Informatics, ICT or related field. People with a degree within healthcare, with proven hands-on knowledge with AI can also apply.
- You have a strong interest in machine learning/AI, and are eager to advance the state-of-the-art.
- Experience with healthcare applications is not obliged, but strong interest is required.
- You are well-organized, able to work independently and take initiative. You respect the predetermined milestones in research projects and set out a work plan to reach them.
- You are a team player, flexible and good communicator.
- Your English is fluent, both speaking and writing.
- Both young graduates and candidates with (industrial) experience are welcome.
How to apply
We offer the opportunity to do full-time research in an international (with over 17 nationalities at IDLab, part of imec and Ghent University) and friendly working environment, with a competitive salary at Ghent University. While grounded in fundamental academic research, as a PhD candidate you will also participate in collaborative research with industrial and/or academic partners in Flanders and/or on a wider geographic scale (e.g., EU H2020 projects), in the framework of new/ongoing projects. Furthermore, you will publish your research results at major international conferences and in journal papers, as part of meeting the requirements for your PhD. The PhD positions are available starting fall 2021.
Send your application by email or any questions concerning this vacancy to prof. Femke Ongenae (firstname.lastname@example.org), indicating “Job Application: Hybrid AI Healthcare” in the subject. Applications should include (1) an academic/professional resume, (2) a personal motivation letter, and (3) transcripts of study results, and (4) at least two reference contacts. After a first screening, selected candidates will be invited for an interview (also possible via Microsoft Teams) as a first contact in a multi-stage selection process. Review of an application starts as soon as it is received. As such, the position may possibly be filled before the deadline.
- Application deadline: 15/07/2021 or until the vacancy is filled.
- Type of contract: Full-time
- Employment: Temporary (4 years), with yearly progress evaluation
- Earliest starting date: July 2021 (but later possible!)