Internship position in Public Health Research (Master2 student) - GA0919
1A-B, rue Thomas Edison, Strassen 1445, Luxembourg
(PD) is a neurodegenerative disease related to aging. This condition is a cause
of death and disability in older individuals. The diagnosis of Parkinson's is
based on motor symptoms such as tremors and bradykinesia. Once motor symptoms
appear, the neurodegenerative process is already advanced. Predictive models of
PD in the general population could be used to detect the population at risk. Predictive
models in individuals at higher risk or with prodromal symptoms may identify
those who are about to start PD or progress rapidly and enable early treatment.
Our team targets to
develop prediction models of PD onset (in the general population) or
progression (in the population at risk) using machine learning techniques. A first step and the primary goal for the student will
be to conduct a systematic review to identify all relevant studies in the area
of risk factors of PD.
Training and Research environment
Chronic diseases, Ageing & Physical Functioning research group is a custom group that belongs to the Department of Population Health, working in association with the Competence Center for Methodology and Statistics, the Bioinformatics Platform. The research activities focus on epidemiology of chronic diseases, prediction and longitudinal trajectories of ageing-related diseases. Master students will assist one of our projects that aims to explore the literature of prediction models for Parkinson disease and will be co-supervised by Dr. G. Aguayo, Dr. S. Schmitz and Ms. C. Dessenne.
What we offer and conditions
- A BSC degree in public health, statistics or similar.
- Basic statistics skills and knowledge of statistical software (ex. R, Stata, SAS).
- English is mandatory
- Students will have the opportunity to work in an interactive and international scientific environment.
- Applicants must be affiliated to their own University Master 2 students will receive 500€/month unless they have their own funding source, e.g. Erasmus grant
REF.: VD/INTPHR0919/GA/PHR CG1
Available for 6-9 months (can be extended according to the specific requirement ot the university- Full time - start date: from now on
1. Hipp G, Vaillant M, Diederich NJ, et al. The Luxembourg Parkinson's Study: A Comprehensive Approach for Stratification and Early Diagnosis. Frontiers in aging neuroscience 2018;10:326. doi: 10.3389/fnagi.2018.00326 [published Online First: 2018/11/14]
2. Darweesh SK, Koudstaal PJ, Stricker BH, et al. Predicting Parkinson disease in the community using a nonmotor risk score. European journal of epidemiology 2016;31(7):679-84. doi: 10.1007/s10654-016-0130-1 [published Online First: 2016/02/24]
3. Butt AH, Rovini E, Dolciotti C, et al. Objective and automatic classification of Parkinson disease with Leap Motion controller. Biomed Eng Online 2018;17(1):168. doi: 10.1186/s12938-018-0600-7 [published Online First: 2018/11/14]
4. Lee A, Gilbert RM. Epidemiology of Parkinson Disease. Neurol Clin 2016;34(4):955-65. doi: 10.1016/j.ncl.2016.06.012 [published Online First: 2016/10/11]
5. Darweesh SK, Koudstaal PJ, Stricker BH, et al. Trends in the Incidence of Parkinson Disease in the General Population: The Rotterdam Study. American journal of epidemiology 2016;183(11):1018-26. doi: 10.1093/aje/kwv271 [published Online First: 2016/05/18]
Dr. Gloria AGUAYO, MD,PhD
1 AB rue Thomas Edison