项目介绍

2024年12月23日

Join Maastricht University as a PhD student in Epidemiology, advancing interdisciplinary methods to ensure reliable AI in healthcare through innovative statistical and health-economic approaches.

Job Description
Artificial intelligence (AI) can support diagnosis and prognosis, and support optimization of patient care. Whether this is achievable depends on the reliability of an AI-model. Testing of AI is often done on small numbers, and AI-models are not equally useful in all locations and patient populations. Reliability can be measured with value-of-information measures that reflect the risks of uncertainty, expressed as the expected number of misdiagnoses. This allows physicians and policymakers to decide for each AI-model: is it ready for use, or is more research needed?

In this project, you will develop and refine these value-of-information measures. In this role, you will conduct interdisciplinary research at the intersection of statistics and health economics to assess and enhance the reliability of AI models in healthcare. To support the dissemination of your methods, you will create user-friendly statistical software, such as an R package. You will share your findings through academic publications, conference presentations, and engagements with stakeholders. Additionally, you will actively contribute to the institute’s academic environment by participating in seminars, workshops, and collaborative research.

Your daily worksite is the Department of Epidemiology at Maastricht University. Your work is part of a VIDI project, which will be carried out by a team of 2 PhD students and one postdoctoral researcher, under the supervision of Dr. Laure Wynants. You will have local collaborators from various departments (epidemiologists, clinicians, statisticians, health-economists, data scientists) and will benefit from access to an international network of experts in clinical risk prediction modelling and value-of-information analysis.

Your day-to-days tasks and responsibilities are to:

• Develop new predictive performance and value-of-information metrics;
• Propose computational algorithms to estimate these metrics;
• Design and execute simulation studies to evaluate the above;
• Develop and test statistical software;
• Deepen subject-matter knowledge trough literature review and active engagement with current research developments;
• Work closely with academic advisors and collaborators to ensure research quality;
• Write up, publish and present findings;
• Assist with teaching duties.

Requirements
You have strong analytical skills and an interest in interdisciplinary research, bridging medical statistics and health-economics. The ideal candidate will be highly self-motivated, with excellent time management skills to balance research, coursework, and deadlines. Strong communication abilities are essential for communicating research findings and collaborating effectively with peers and supervisors. The candidate should demonstrate resilience and adaptability, managing challenges and feedback with a positive, solutions-oriented approach. Teamwork, critical thinking, and the ability to work independently with a pro-active attitude will be key to thriving in this dynamic research environment.

The candidate:

• Has a Master’s degree in Statistics, Health-Economics, or equivalent;
• Has knowledge of / experience with one or more of:
  o validation of diagnostic or prognostic models created with regression or supervised machine learning;
  o Health technology assessment of diagnostic or prognostic models created with regression or supervised machine learning;
  o decision curve analysis;
  o value-of-information analysis.
• Has a strong interest to bridge different academic or practical disciplines,
• Masters scientific English for speaking, reading, and writing,
• Can program in R or is proficient in another language and willing to learn R.

Knowledge of or experience in clinical epidemiology is considered an asset.

What we offer
As a PhD candidate at the Faculty of Health, Medicine and Life Sciences, you will be employed by the most international university in the Netherlands, located in the beautiful city of Maastricht. In addition, we offer you:

• Good employment conditions. The position is graded according to UFO profile PhD, with corresponding salary based on experience ranging from €2901,00 in the first year and €3707,00 gross per month in the fourth year (based on a full-time employment of 38 hours per week). In addition to the monthly salary, an 8.0% holiday allowance and an 8.3% year-end bonus apply.
• An employment contract for a period of 12 months with a scope of 1,0 FTE. Upon a positive evaluation, an extension of 3 years will follow. 
• At Maastricht University, the well-being of our employees is of utmost importance, we offer flexible working hours and the possibility to work partly from home if the nature of your position allows it. You will receive a monthly commuting and internet allowance for this. If you work full-time, you will be entitled to 29 vacation days and 4 additional public holidays per year, namely carnival Monday, carnival Tuesday, Good Friday, and Liberation Day. If you choose to accumulate compensation hours, an additional 12 days will be added. Furthermore, you can personalize your employment conditions through a collective labor agreement (CAO) choice model. 
• As Maastricht University, we offer various other excellent secondary employment conditions. These include a good pension scheme with the ABP and the opportunity for UM employees to participate in company fitness and make use of the extensive sports facilities that we also offer to our students. 
• Last but certainly not least, we provide the space and facilities for your personal and professional development. We facilitate this by offering a wide range of training programs and supporting various well-established initiatives such as ‘acknowledge and appreciate’. 
   

The terms of employment at Maastricht University are largely set out in the collective labor agreement of Dutch Universities. In addition, local provisions specific to UM apply. For more information, click here.

Maastricht University
Why work at Maastricht University?

At Maastricht University (UM), everything revolves around the future. The future of our students, as we work to equip them with a solid, broad-based foundation for the rest of their lives. And the future of society, as we seek solutions through our research to issues from all around the world. Our six faculties combined provide a comprehensive package of study programmes and research.

In our teaching, we use the Problem-Based Learning (PBL) method. Students work in small groups, looking for solutions to problems themselves. By discussing issues and working together to draw conclusions, formulate answers and present them to their peers, students develop essential skills for their future careers.

With over 22,300 students and more than 5,000 employees from all over the world, UM is home to a vibrant and inspiring international community.

Are you drawn to an international setting focused on education, science and scholarship? Are you keen to contribute however your skills and qualities allow? Our door is open to you! As a young European university, we value your talent and look forward to creating the future together.

Click here for more information about UM. 

Faculty of Health, Medicine and Life Sciences
At the Faculty of Health, Medicine and Life Sciences (FHML), everything revolves around healthy living. Our research and education are not solely focused on recovery, but place a strong emphasis on prevention, health preservation, and health promotion. Our aim is to use our knowledge and expertise to genuinely contribute to the well-being of individual people as well as society in total. 

In, research, and healthcare, FHML is closely collaborating with the academic hospital in Maastricht, together forming the Maastricht University Medical Center (MUMC+).  FHML is strongly connected in education, research, and care with the Maastricht academic hospital, together with which it forms the Maastricht University Medical Centre (MUMC+). 

FHML, which is the largest faculty of Maastricht University, is formed by an (inter)national community of employees and students. The faculty offers a wide range of Dutch and English-taught bachelor’s and master’s programs in innovative educational concepts in which the emphasis is always on building bridges to practice.
 

The multidisciplinary research of the FHML focuses on a number of carefully chosen topical current themes. In addition to research aimed at gaining new insights, it also concerns research whose results can be directly translated into concrete applications and innovations. The implementation of the various research programs is organized in our six graduate schools and two institutes.

 
Department
The mission of the Department of Epidemiology of the Faculty of Health, Medicine and Life Sciences, Maastricht University, is to improve human health and well-being through epidemiologic research and teaching. Our research aims to develop, improve and validate tools and strategies for etiology, prevention, diagnosis, treatment and care. Our research is user-relevant and multidisciplinary, and applies both observational and experimental designs.

Curious?
Are you interested in this exciting position but still have questions? Feel free to contact Dr. Laure Wynants at laure.wynants@maastrichtuniversity.nl for more information.

Applying?
Or are you already convinced and ready to become our new PhD candidate? Apply now, no later than 12 January 2025, for this position.
 

The vacancy is open for internal and external candidates. In case of equal qualifications, internal candidates will be prioritized.

Maastricht University is committed to promoting and nurturing a diverse and inclusive community. We believe that diversity in our staff and student population contributes to the quality of research and education at UM, and strive to enable this through inclusive policies and innovative projects led by teams of staff and students. We encourage you to apply for this position.