PhD Program in Health Data Sciences
The newly established PhD Program in Health Data Sciences at the Charité is aimed at qualified young scientists interested in:
- deepening their methodological knowledge in the fields of biostatistics, epidemiology, metaresearch and/or population health science
- further expanding their competence in research and teaching
Upon successful completion of the program, students will be awarded the academic degree of "Doctor of Philosophy" (PhD).
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- Six semesters (full time)
- 180 ECTS: scientific research project (150 ECTS) and accompanying scientific coursework in the HDS disciplines of biostatistics, epidemiology, meta-research, population health science and public health (30 ECTS)
- All scientific coursework offered through the program will be taught in English. Other university courses at the Charité may also be counted towards the 30 ECTS requirement, if approved.
- The PhD program is free of charge with the exception of the Charité enrollment fee
- Next possible start of program: Spring 2019 (Summer Semester).
- Applications for Spring 2019 start date due October 1, 2018.
Admission to the HDS PhD program is competitive and students must meet the following requirements to be considered for acceptance:
- Proof of eligibility for a doctoral degree per the requirements outlined by the Charité - Universitätsmedizin Berlin (Promotionsberechtigung)
- Evidence of prior course work with professional relevance to Health Data Sciences (biostatistics, epidemiology, meta-research and/or population health science)
- A strong command of the English language (minimum B2 level based on the Common European Framework of Reference for Languages, please find details here)
- Secured funding for the duration of the doctorate program:
As this is a full-time program, the possibilities of an additional secondary occupation are limited. At the current time, we are not able to offer scholarships, but this may change in the future. We recommend that students consider how they plan to finance their living expenses for the duration of their studies before applying.
Beyond the formal requirements, students demonstrating exceptional motivation for their research topic(s) of interest will be prioritized. Interested students are strongly encouraged to contact potential advisors in their field prior to applying.
The HDS doctoral program committee will conduct interviews in the spring and fall of each year. Admitted applicants may formally enroll in the program the following semester.
About the program
The goal of the multidisciplinary doctoral program in Health Data Sciences for outstanding junior researchers is to provide a structured framework of studies, research, and teaching for promising scientists in the disciplines of Health Data Sciences (HDS). The program places particular emphasis on preparing students for future careers at universities, non-university research institutions, and other scientific public or private institutions in the fields of biostatistics, epidemiology, meta-research, population health science and public health.
The program Health Data Sciences is under the direction of Prof. Dr. Dr. Tobias Kurth, Institute of Public Health, Prof. Dr. Geraldine Rauch, Institute of Biometry and Clinical Epidemiology and Prof. Dr. Ulrich Dirnagl, BIH Center for Transforming Biomedical Research (QUEST).
The scientific coursework serves to deepen methodological knowledge, instill the fundamentals of sound scientific research practice, and promote effective dissemination of knowledge through teaching. Focus areas:
Doctoral students attend courses in HDS domains to learn advanced health data sciences methods they will later apply in a practical research setting for their individual research projects.
Interactive seminars and workshops serve to support doctoral candidates in all stages of their individual research project work ultimately resulting in three scientific publications.
Under the guidance of experienced HDS faculty members, doctoral students prepare and teach courses focused on relevant topics.
Applications for the PhD program must be submitted to the PhD Committee before the posted deadline and should include the following documents. All documents should be in English:
- The application form.
- Motivational letter in English. This letter should convey the applicant's scientific interests, academic achievements and professional background.
- Curriculum vitae.
- Copies Bachelor's and Master's degree certificates. Alternatively, comparable university degree certification or copies of other documents proving the entitlement to a doctorate may be submitted.
- A letter of reference from a university professor or lecturer (in English or German).
- Proof of knowledge of the English languageat least at level B2 or equivalent of the Common European Framework of Reference for Languages.
After reviewing all applications, the PhD Committee will invite applicants eligible for admission to participate in selection interviews and presentations. These interviews will be conducted by a selection committee appointed by the PhD Committee and last approximately 25 minutes each, of which applicants will give a 15-minute presentation about their previous scientific education, accomplishments, and planned research project.
Annual application deadline: 30th of April each year.
The selection interviews will be scheduled to take place on approximately two months later. Applicants will be notified via email regarding the timing of their interview/presentation.
Please send your application documents as an attachment (ideally as one single PDF document) to the HDS email address.
Health Data Sciences
Health Data Sciences (HDS) is an emerging scientific discipline, fusing the strengths of multiple research fields including biostatistics, epidemiology, meta-research, and population health science. HDS employs a synergistic approach to provide comprehensive, evidence-based solutions to complex, real-world health problems using various data resources.
By employing innovative analytical tools, HDS methodology provides insight both into patterns within data as well as possible underlying causal structures. Its comprehensive modeling approaches combine critical and conceptual reasoning with the latest advances in computer science to ensure accurate analysis and interpretation of data as well as effective communication of reliable conclusions for health promotions.