The training program of the HIDSS4Health is designed as a balanced combination of introductory data science fundamentals, relevant interdisciplinary courses, and active research training within the specific application domains and educational track.
Overview
The goal of the HIDSS4Health curriculum is that its doctoral researchers become experts in data science and in at least one health-related field. It will include the ability to communicate with experts from both sides and to transfer knowledge from data to life science and vice versa. All lectures, courses, retreats and summer schools are held in English.
Supervision Concept
All doctoral researchers will receive feedback from a Thesis Advisory Committee (TAC). This consists of an interdisciplinary team of at least three Principal Investigators (PI), including PIs with data science and life science backgrounds from different institutions. TAC meetings will discuss the progress of the doctoral research project, the publication concept, working plans, cooperation potential with other doctoral researchers, planning of the mobility phase, as well as the supervision quality and career perspectives of the doctoral researcher. Although each doctoral researcher will belong to the main supervisor’s group, we recommend an associate membership in the group of a second PI, including an official second affiliation, regular stays in this group (such as once a week or one week per month) and integration in lab meetings etc. We anticipate that this will foster closer integration between data and life science expertise and a deeper understanding of both aspects.
I love how interdisciplinary it is here. Even though it has become something of a buzzword, the interplay between multiple disciplines is in fact an everyday reality in my PhD.
Alexandra Walter
Degree
In general, the doctoral thesis degree will be awarded by the informatics faculty of KIT or Heidelberg University, depending on your main supervisor’s individual affiliation. Each doctoral researcher who successfully finishes all training modules and the doctoral thesis project will receive a certificate as “Data Scientist”, including a confirmation regarding specialization in the health-related application domain. This certificate is a joint activity of the Faculty for Informatics at KIT and the Faculty for Mathematics and Computer Science at Heidelberg University.
Lectures, Workshops, Seminars
All incoming doctoral researchers start with the interdisciplinary course “Data Science & Health”. This is held by the PIs of the school and invited speakers who cover additional topics. The course consists of lecture-style elements and an interactive discussion and covers data science methods (such as clustering, image analysis, visualization, deep learning), domain-oriented methods (such as medical imaging, surgery) as well as ethical, legal and social implications. In addition, it will train researchers to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.
The following lecture course "Advanced Topics in Data Science & Health" has two objectives. On the one hand, it provides advanced doctoral researchers and postdocs with the opportunity to gain some experience in this type of teaching format. On the other hand, it will provide first-year doctoral researchers with an overview of recent scientific developments and the state of the art in the research fields of the presenters.
In addition, we will offer a set of specific scientific courses (for example, on Simulation & Optimization, Machine Learning, Uncertainty Quantification, Scientific Visualization, Python, Confocal Microscopy, International Zebrafish and Medaka Course) with a data and/or life science orientation.
The exact configuration of these compulsory elective courses is individually chosen by the doctoral researcher depending on thematic needs and existing skills.
“There is great energy coming from the collaboration. Researchers who almost never talked to each other before are now coming together quite automatically.”
Klaus Maier-Hein, spokesperson of HIDSS4Health
Data Science & Health - Winter Semester 2024/2025
Data Science & Health - Winter Semester 2024/2025
This lecture is held by the PIs of the school and invited speakers who cover additional themes. The course consists of lecture-style elements and an interactive discussion. It covers data science methods (e.g., clustering, image analysis, visualization, deep learning), domain-oriented methods (e.g., medical imaging, surgery) as well as ethical, legal and social implications.
The Lecture Data Science & Health
The aim of this course is to introduce the doctoral researchers to the field and to give a basic understanding of all research areas of the school. In addition, it will train them to consider the different views and needs of data science and health-related sciences, and the opportunities for interdisciplinary cooperation.
Organization: The lecture Data Science & Health is offered online via zoom. The lecture takes place weekly and only attendance is documented. Currently, no further examinations are planned. Registration is required, a registration link can be requested via mail to office@hidss4health.de.
Please note: Currently, no dates have been set. We will provide timely information about the lectures.
Exchange Program
Each doctoral researcher of the school should complete a funded mobility phase of around three months, either at a leading international university or institution. The school supports the selection of appropriate partner institutions, for example, using existing international cooperation networks such as:
- HeKKSaGOn (the Heidelberg – Kyoto – Karlsruhe – Sendai – Göttingen – Osaka network of German and Japanese universities)
- InterACT (network of eight universities from Europe, Asia and America including KIT and Carnegie Mellon University).
DKFZ has exchange programs with the Weizmann Institute of Science (Israel), the MD Anderson Cancer Center (USA) and partners of Cancer Core Europe.
Summer Schools
Each doctoral researcher is expected to attend at least one summer school, which will be held annually. These summer schools will be co-organized with other data science schools within HIDA, thus enabling all Helmholtz Information and Data Science School participants to network and encourage further collaboration.
"I am very curious and, therefore, love that the school is so interdisciplinary. I work with colleagues from medicine who never get tired of answering all my questions."
Elaine Zaunseder
Personal Skill Training
The school provides a wide portfolio of personal skills training (including leadership and language) as well as comprehensive support measures regarding internationalization, networking and career orientation. Courses such as “Introduction to German Academic Culture”, “Publishing in Scientific Journals”, and “Managing Projects” are currently offered. Furthermore, research school doctoral researchers will be encouraged to participate in the centrally-organized Helmholtz Transferable Skills Training program for doctoral researchers. Three modules, each lasting 2–3 days will provide training in
- research skills development
- presentation and communication
- career and leadership.
Cross Theme Topics
Cross Theme Topics (CTTs) are a collaboration between 2–4 doctoral researchers working on related methodological problems, supported and mentored by postdocs and PIs of the school. The aim of a CTT is to leverage synergy potentials between projects (for example by sharing special skills of a doctoral researcher between projects) and to broaden the individual methodological understanding beyond the research areas. The ideal outcome of a CTT would be a joint paper, software tool or benchmark dataset. All doctoral researchers in HIDSS4Health will be encouraged to be involved in at least one CTT during his/her time in the school.
