
Are you interested in bringing artificial intelligence and data science to the forefront of health research? Three top-level institutions – the Karlsruhe Institute of Technology, the German Cancer Research Center and Heidelberg University – have joined forces to tackle this exciting area by providing a unique doctoral program. Send your application if you can see the potential in techniques such as deep learning in the health sector and if you are ready to shape the future of our well-being.




Mission Statement
The aim of the Helmholtz Information & Data Science School for Health (HIDSS4Health) is to attract, promote and train the best young talents at the interface between data science and health-related applications. HIDSS4Health offers a structured doctoral training program embedded in a highly interdisciplinary research environment, bringing together experts from the data and life sciences. The scientific curriculum is complemented by training measures that provide doctoral researchers with the key qualifications expected from future leaders in science and industry.

Research Areas




Projects
Projects 2020 (see also the proposals)
Imaging & Diagnostics

Cell segmentation and tracking is the problem of processing a time series of (3D) images showing development of an organism (e.g... (read more)

Tumour hypoxia, a state of low oxygen levels in certain tissue regions, seems to play a prognostic role for loco-regional tumour... (read more)
Surgery & Intervention 4.0

The precise spatial delineation of cancerous and healthy tissue in radiation therapy is necessary to prevent side effects and the reoccurrence... (read more)

Laparoscopic surgery is a team effort. A surgeon and her assistant(s) collaborate to solve a shared task working individually and as... (read more)
Models for Personalized Medicine

As it is becoming progressively challenging to wholly analyse the ever-increasing amounts of generated biomedical data (e.g., CT scans, X-ray images... (read more)

In recent years, biomedical data have been increasingly available. In particular, the costs for procuring omics data, including genome and exome... (read more)
Projects 2019
Imaging & Diagnostics

While Deep Learning approaches, in particular Convolutional Neural Networks (CNNs), are now also being widely adopted in medical image analysis, only... (read more)

The optimization of radiofrequency (RF) transmit coils for magnetic resonance imaging (MRI) is a highly complex and computationally expensive procedure, particularly... (read more)

Current deep learning (DL) based image segmentation methods are commonly trained to give maximum likelihood estimates, while many real-world problems suffer... (read more)

Fluorescence microscopy has several inherent limitations, which are dictated by basic optical and physical laws as well as compromises arising from... (read more)
Many biomedical imaging devices are able to collect time series of high resolution 3D data (3D+t). A typical task in these... (read more)

Compared to man-made technical control systems, biological control systems exhibit a remarkable fault tolerance over wide ranges of environmental cues as... (read more)

The image shows the challenge of the project in my view: the first part shows a volume rendering of a lung... (read more)

An assumption-free automatic check of medical images for potentially overseen anomalies would be a valuable assistance for a radiologist. Deep learning... (read more)
Surgery & Intervention 4.0
Replacing traditional open surgery with minimally-invasive techniques for complicated interventions such as partial tumor resection or anastomosis is one of the... (read more)

Radiation therapy is one of the cornerstones in modern cancer treatment being applied in 50 % of all patients. It is a... (read more)
Current Machine Learning algorithms, especially Deep Neural Networks (DNNs), have shown to be successful tools in areas particularly relevant for Life... (read more)

Photoacoustic imaging (PAI) is an emerging modality that has the potential to provide tomographic images of blood oxygenation - an important... (read more)
Models for Personalized Medicine

Modern life sciences with their highly sensitive omics data sets face several challenges with regard to data storage and sharing. On... (read more)

With the possibility of whole genomic sequencing for oncologic patients, many processes in their treatment have to be adapted. Physicians in... (read more)
Multi-omics, the generation of omics-profiles with multiple assays on the same set of biological samples, is a fundamental experimental design pattern... (read more)

Steering Committee
Spokespersons
Elected Members
The Helmholtz Information & Data Science Academy
As domain scientists from all research fields need to be equipped with knowledge, methods and tools in the areas of Information & Data Science, a training and education initiative for researchers in the Helmholtz Association has been established. The Helmholtz Information & Data Science Academy (HIDA) will coordinate networking, education and training activities throughout the entire Helmholtz Association. It bundles the resources of all Information & Data Science Schools (HIDSS), including HIDSS4Health.

Coordination Office
Coordination

Data Science Groups

Life Science Groups

Our aim is to obtain genomes from new bacterial and archaeal taxa with no sequenced representatives and providing direct link information between cell's phylogenetic and metabolic markers by matching phylogeny and function. We unravel novel metabolisms, ecophysiology and mechanisms of energy conservation among uncultured microorganisms and in addition investigating minimal genome requirements and syntrophic interactions as well as genetic and phenotypic heterogeneity among cells, cell-to-cell variations, horizontal gene transfer and evolutionary pressure.

The Durstewitz group develops statistical machine learning methods at the mathematical and algorithmic levels, with a focus on nonlinear time series analysis, nonlinear dynamical systems, and generative recurrent neural networks. Our main application domains are functional neuroimaging and electrophysiological data, as well as smartphone-based ecological momentary assessments, in psychiatry and neurology.

Our research focuses on (a) pattern recognition in biological data, multi-omics analysis and data integration with special application to cancer genomics as well as (b) translational and personalized oncology, molecular tumor boards, biomarker development and data analysis for clinical trials.

We develop methods and tools enabling data-driven insights into cancer biology through the epigenomes of tumor cells. Our major research topics include integrative analysis of large epigenomic data sets, deconvolution approaches for tumour heterogeneity inference, novel experimental techniques for epigenome profiling, as well as generic infrastructure for reproducible bioinformatics.

Our main aim is to develop and apply computer-aided methods to study how biomolecules, such as proteins, interact. The methods make use of three-dimensional macromolecular structures and combine multiscale approaches based on physicochemical principles with those of bio-/chemo-informatics and machine learning.

Training Summary
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.
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.
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) and 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.
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 (I) research skills development, (II) presentation and communication and (III) 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.

Training Program
Lectures
Data Science & Health – Winter Semester 2020/2021
⎙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 aim of the 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
Data Science & Healthis offered online. The lecture takes place every two weeks 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.
Dates ⓘ
Date | Time | Location | Lecturer | Topic |
---|---|---|---|---|
2020-11-02 | 14:00–15:30 | Zoom | Bogdan Savchynskyy | Combinatorial Optimization Techniques for Bioimaging |
2020-11-02 | 15:45–17:15 | Zoom | Frank Ückert | Medical Informatics in Translational Oncology |
2020-11-16 | 14:00–15:30 | Zoom | Ralf Mikut | Time Series Analysis |
2020-11-16 | 15:45–17:15 | Zoom | Alexander Schug | Data Inference on Sequence Data |
2020-11-30 | 14:00–15:30 | Zoom | Holger Fröning | Any Growth is Bounded – On the Future of Performance Scaling |
2020-11-30 | 15:45–17:15 | Zoom | Benedikt Brors | The n<<p paradigm in omics data analysis |
2020-12-14 | 14:00–15:30 | Zoom | Franziska Mathis-Ullrich | Minimally-Invasive Robots for Medicine |
2020-12-14 | 15:45–17:15 | Zoom | Martin Frank | Mathematical Foundations of Deep Learning |
2021-01-11 | 14:00–15:30 | Zoom | Michael Beigl | Wearable and mobile health data |
2021-01-11 | 15:45–17:15 | Zoom | Filip Sadlo | Introduction to Visual Data Science |
2021-01-25 | 14:00–15:30 | Zoom | Peter Sanders | Parallel Algorithms for Dummies |
2021-01-25 | 15:45–17:15 | Zoom | Achim Streit | Introduction to Distributed and Parallel Computing |
2021-02-08 | 14:00–15:30 | Zoom | Lena Maier-Hein | Surgical Data Science |
2021-02-08 | 15:45–17:15 | Zoom | Michael Gertz | Trends and Topics in Text Analytics |
Data Science & Health – Winter Semester 2019/2020
⎙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 aim of the 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 lectures take place at the following locations:
- KIT: Geb 30.28, seminar room 004, Wolfgang-Gaede-Straße 6, 76131 Karlsruhe
- Mathematikon: Interdisziplinäres Zentrum für Wissenschaftliches Rechnen IWR, conference room 5.104, Im Neuenheimer Feld 205, 69120 Heidelberg
- DKFZ: main building, room H824 (8th floor), Im Neuenheimer Feld 280, 69120 Heidelberg
Dates ⓘ
Date | Time | Location | Lecturer | Topic |
---|---|---|---|---|
2019-10-22 | 09:45–11:15 | KIT | Ralf Mikut | Time Series Analysis |
2019-10-22 | 11:30–13:00 | KIT | Lennart Hilbert | Fluorescence microscopy and digital image processing in molecular cell biology |
2019-11-05 | 09:45–11:15 | Mathematikon | Carsten Rother | Image-based Machine Learning |
2019-11-05 | 11:30–13:00 | Mathematikon | Klaus Maier-Hein | Radiologic Data Science |
2019-11-19 | 09:45–11:15 | KIT | Achim Streit | Introduction to Distributed and Parallel Computing |
2019-11-19 | 11:30–13:00 | KIT | Peter Sanders | Parallel Algorithms for Dummies |
2019-12-03 | 09:45–11:15 | Mathematikon | Holger Fröning | Any Growth is Bounded – On the Future of Performance Scaling |
2019-12-03 | 11:30–13:00 | Mathematikon | Lena Maier-Hein | Surgical Data Science |
2019-12-17 | 09:45–11:15 | KIT | Franziska Mathis-Ullrich | Minimally-Invasive Robots for Medicine |
2019-12-17 | 11:30–13:00 | KIT | Tamim Asfour | Data-Driven Learning of Sensorimotor Skills for Robots and Exoskeletons |
2020-01-07 | 09:45–11:15 | Mathematikon | Michael Gertz | Introduction to Text Analysis |
2020-01-07 | 11:30–13:00 | Mathematikon | Robert Strzodka | Essential Performance Considerations in Programming |
2020-01-21 | 09:45–11:15 | KIT | Anne Koziolek | Requirements Engineering for Data-driven Solutions |
2020-01-21 | 11:30–13:00 | KIT | Ali Sunyaev | Introduction to Decentralized Data Management with Distributed Ledger Technology |
2020-02-04 | 09:45–11:15 | DKFZ | Martin Frank | Mathematical Aspects of Uncertainty Quantification |
2020-02-04 | 11:30–13:00 | DKFZ | Filip Sadlo | Introduction to Visual Data Science |
Seminars
Publishing in Scientific Journals
The seminar „Publishing in scientific journals“ covers all aspects of writing, submitting, reviewing and publishing your scientific manuscript. Participants will be equipped with a framework to prepare manuscripts more efficiently and effectively. They will become familiar with writing techniques to improve their text. They will be prepared to interact with editors and deal with reviewers‘ comments and participants will have identified ways to increase the impact of their research. Possible topics of this two-day seminar with many writing exercises include:
- Planning stage: structure and focus
- Preparing the sections of a manuscript
- Tips for professional writing
- Title and abstract
- Publication strategy
- Increasing your impact
Source: https://www.nawik.de/seminare/seminars-in-english/
Next: October 13th/14th 2020, 9-17, KIT, Karlsruhe - Fully booked. Next course planned for spring 2021.
Presentation Skills Course
Scientists are more often required to present their work in various situations – at scientific conferences, in lab meetings, to collaborators, as well as at open days and other outreach events.
Participants of this two-day seminar learn the theoretical basics as well as applicable, practical knowledge of science communication. They practice specialized presentation competencies and experience the impact of successful presentations to different target groups.
With the help of video analysis, the participants identify their individual strengths and step by step optimize their skills of communication within and outside the academic environment. They also learn how to react to critical questions from the plenum. Possible topics of this seminar include:
- From senders and receivers – layers of communication
- Formulating your core message
- Creativity as key to success
- Lecture development methods
- Body language, composure, facial expression, gestures
- Responding to questions from the audience
Source: https://www.nawik.de/seminare/seminars-in-english/
Next: May 18th/19th 2021, Heidelberg, DKFZ.

Apply
Overall Process
We will perform an annual candidate selection process. We will select candidates in a four-step process: (1) the written application, (2) an optional Skype interview for shortlisted candidates, (3) an on-site selection event, and (4) matching projects and candidates. Evaluation criteria for the candidates are an excellent academic record and a strong motivation for the interdisciplinary nature of the projects. The institutions are committed to increasing the percentage of female scientists and encouraging female applicants to apply.
January 18, 2021 | Start of next application round |
March 1, 2021 | Deadline for written applications |
Apr 23, 2021 | Selection Event in Heidelberg |
May 1 – Sep 1, 2021 | Start of Doctoral Researchers |
Winter semester 2021/2022 | Start Courses & Training Program |
Living in Heidelberg / Karlsruhe
Heidelberg and Karlsruhe are attractive cities located in the sunny south-western part of Germany within a traveling distance of around 50 kilometers. They are connected via a dense public transport network. More information on living costs can be found in the information pages of the German Academic Exchange Service (DAAD) or in a PDF booklet from the German Federal Ministry for Education and Research.
Requirements
We are looking for excellent graduates holding master degrees, received by July 2020 at the latest, in computer science, mathematics, engineering, physics or related quantitative sciences (e.g., bioinformatics or medical informatics).
Funding
All positions are fully-funded (e.g. “E13” TVöD or TV-L positions under applicable regulations of the participating institutions) according to the public sector salary system at German universities and research centers. It means a yearly gross income of more than 45.000 € and results in more than 26.000 € net income after taxes, own contributions to health insurance, pension insurance, unemployment insurance etc. Weekly working times are 39.5 hrs, the contract includes 30 paid leave days. However, since the German system is quite complicated, all financial details cannot be provided here but can be calculated here (sorry, only available in German).
Becoming an Associated Doctoral Researcher
You are already funded and your doctoral research is on a topic that is relevant to the school? Apply for an association!
Diversity and Equal Opportunities
The provision of equal opportunities and diversity is a central concern of KIT, DKFZ and Heidelberg University. We are committed to enabling researchers to balance the demands of career and family life and therefore offer various options to staff, such as flexible working time models, family-friendly meeting times, re-entry after maternity leave, comprehensive childcare concepts, provision of Tele-offices, parent-child offices, and in-house childcare facilities. Funded places in local childcare facilities offer full-time and part-time child-care (for example during a conference or summer school). The networks of female scientists at the partners – “WiKIT” at the KIT and the Executive Women’s Initiative at the DKFZ – are professional networks of female scientists in leading positions. These networks are platforms of mutual exchange and aim at fostering networking among female scientists of various disciplines to improve the career perspectives and working conditions of female scientists in Germany. All measures promoting the compatibility of career and family life are monitored and advanced by the “familiengerechte Hochschule” (family-friendly university) audit and “Audit Beruf und Familie” (Career and Family Audit) at the KIT, Heidelberg University and DKFZ, respectively.

Events
Upcoming Events
HIDSS4Health Selection Event
- 1 day
- Online
Second round internal selection event for invited candidates (online format).
HIDSS4Health Retreat
- 2 days
- Bad Liebenzell
First retreat for doctoral researchers of the school.
Bioinformatics Career Day
- 1 day
- Heidelberg
More information will be available soon on www.dkfz.de/careerday.
The Machine Learning Summer School
- 13 days
- Tübingen
More information about the school and the application procedure can be found here (the old program of 2017 may be of interest).
Summer school on deep learning for medical imaging
- 8 days
- Montreal
More information about the school and the application procedure can be found here. Please note that the International Conference on Medical Imaging with Deep Learning follows directly after the school and might also be of interest.
ICVSS 2020 (International Computer Vision Summer School)
- 7 days
- Sicily
More information about the school and the application procedure can be found here.
Machine Learning Summer School-Indonesia
- 7 days
- Indonesia
More information about the school and the application procedure can be found here.
Helmholtz Virtual Data Science Career Day
- 1 day
- Online
- Website
Publishing in Scientific Journals
- 2 days
- Karlsruhe
- Website
AI in Healthcare Symposium
- 2 days
- Heilbronn
- Website
The symposium brings together researchers from across the globe to address the promises and challenges of artificial intelligence in healthcare.
HIDA Datathon for Grand Challenges on Climate Change
- 2 days
- Berlin
- Website
Mediterranean Machine Learning summer school
- 6 days
- Milan
- Website
Presentation Skills Course
- 2 days
- Heidelberg
- Website
HIDSS4Health Retreat
- 3 days
- Bad Herrenalb
Retreat for doctoral researchers of the school.
Yearly Overview

News

Contact
Office Karlsruhe
Karlsruhe, Germany
Phone: +49 721 608-25731
Email: office@hidss4health.de
Office Heidelberg
Heidelberg, Germany
Phone: +49 6221 42-2327
Email: office@hidss4health.de