Helmholtz Information & Data Science School For Health
KIT
Heidelberg Unviversity
DKFZ

Do you want to bring artificial intelligence and data science to the forefront of health research? Three top institutions - the Karlsruhe Institute of Technology, the German Cancer Research Center and Heidelberg University - have joined together to tackle this thrilling area by providing a unique doctoral program. Apply 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


In Imaging & Diagnostics, we employ machine or deep learning to exploit increasingly large and complex datasets generated by current high-throughput technologies in medicine, biology and health-related biotechnology. We will have to deal with various challenges including real-time conditions, quantification of uncertainty and ambiguity in imaging and omics data as well as the development of explainable decision making.

In Surgery & Intervention 4.0, we focus on the role of data science in robot- and computer-assisted surgery and interventions. It includes the development and use of computational methods for planning and automation of examinations, surgery and interventions of different types and for intelligent assistive systems collaborating with the physicians, guiding them or supporting their learning process.

In Models for Personalized Medicine, we plan to integrate data-driven modeling, simulation, and visual exploration with first principles modeling. It includes models for real-time applications or patient models for an interactive visualization. Data to be considered include text data (e.g., intervention logs, admission notes), time series data, features extracted from images or omics data, as well as more traditional numerical data (e.g., lab results).

Governance


Spokespersons

Random Name

Ralf Mikut

Karlsruher Institute of Technology

Homepage

Random Name

Klaus Maier-Hein

German Cancer Research Center

Homepage

Random Name

Katja Mombaur

Heidelberg University

Homepage

The Helmholtz Information & Data Science Academy

Since domain scientists from all research fields need to be equipped with knowledge, methods and tools of Information & Data Science, a training and education initiative for researchers in the Helmholtz Association was 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.

Data Science Groups


Random Name

Achim Streit

The Streit team is enabling data-intensive science through generic informatics R&D in the areas data management, data analytics, federated computing and scheduling.

Homepage  Details

Random Name

Ali Sunyaev

The Sunyaev team designs, develops, and evaluates reliable and purposeful software and information systems within the scope of information security solutions and innovative health IT applications.

Homepage  Details

Random Name

Alin Albu-Schäffer

The Albu-Schäffer team addresses in cooperation with the Asfour team medical robotics and the related data acquisition and Interpretation.

Homepage  Details

Random Name

Anne Koziolek

The Koziolek team researches how to ease development of data-intensive systems, esp. the selection of appropriate machine learning algorithms with respect to suitability and performance.

Homepage  Details

Random Name

Benedikt Brors

The Brors team develops algorithms for personalized medicine, cancer epigenetics and single-cell sequencing and applies them in a clinical context as well as in large international consortia.

Homepage  Details

Random Name

Carsten Dachsbacher

The research group developes methods for interactive visualization, high performance computer graphics, and radiative transport simulations.

Homepage  Details

Random Name

Carsten Rother

We (C. Rother, B. Savchynskyy, U. Köthe) work on machine learning and combinatorial optimization (IP, LP) - e.g. 2D/3D tracking, explainable ML, NN+Graphical Models

Homepage  Details

Random Name

Dorothea Wagner

The Wagner team works on graph algorithms and algorithm engineering with applications in mobility, energy and social networks.

Homepage  Details

Random Name

Filip Sadlo

The Visual Computing Group develops novel techniques for visual data analysis, with a focus on topological analysis and feature extraction.

Homepage  Details

Random Name

Frank Ückert

MITRO researches innovative IT concepts with tools like AI, bringing together care and research while improving the international research landscape.

Homepage  Details

Random Name

Hannes Kenngott

The Kenngott Team develops computer-assisted surgery, clinical decision support systems, medical robotics, cyber-physical systems in surgery and machine learning on medical images and data.

Homepage  Details

Random Name

Holger Fröning

Performance, energy-efficiency and programmability: HPC/HPA, deep learning, reconfigurable logic. Example 1: deep learning on embedded systems. Example 2: simplified multi-GPU programming

Homepage  Details

Random Name

Klaus Maier-Hein

The Maier-Hein team develops machine learning algorithms, mathematical modelling approaches for computational image understanding and large-scale information processing.

Homepage  Details

Random Name

Lena Maier-Hein

The Maier-Hein team aims to improve the quality of interventional healthcare and its value computationally. It supports the physician throughout the entire process of disease diagnosis, therapy and follow-up with the right information at the right time.

Homepage  Details

Random Name

Martin Frank

The Frank group aims at bringing modern mathematical techniques into practice. These techniques include modeling, simulation, optimization, inverse problems and uncertainty quantification.

Homepage  Details

Random Name

Michael Beigl

The Beigl team develops AI-driven analytics and Big Data methods and systems to solve problems in application domains.

Homepage  Details

Random Name

Michael Gertz

The Gertz team focuses on novel models and techniques in support of information extraction, data/text mining, machine learning, and network analysis for heterogeneous data.

Homepage  Details

Random Name

Oliver Stegle

The Stegle group develops and applies statistical approaches and methods based on machine learning for analysing high-dimensional molecular data modalities.

Homepage  Details

Random Name

Peter Sanders

The Sanders team develops basic toolbox algorithms and software libraries for handling large data sets in a scalable way.

Homepage  Details

Random Name

Rainer Stiefelhagen

The Stiefelhagen team investigates methods to analyse images using weak supervision, for example by jointly analysing medical images and their associated clinical reports.

Homepage  Details

Random Name

Ralf Mikut

The Mikut team analysis 2D, 3D and 3d+t biomedical images and related data using image analysis and machine learning methods.

Homepage  Details

Random Name

Robert Strzodka

The chair Application Specific Computing focuses on efficient interactions of mathematic, algorithmic and architectural aspects in heterogeneous high performance computing (GPUs, FPGAs, many-core).

Homepage  Details

Random Name

Tamim Asfour

The Asfour team develop data-driven methods and algorithms for skill learning, motion generation and prediction in the context of robot-assisted surgery and exoskeletons.

Homepage  Details

Life Science Groups


Random Name

Alexander Schug

The Schug lab develops and applies methods for molecular simulation and analysis of genomic data to address questions in biological and medical research.

Homepage  Details

Random Name

Christof M. Niemeyer

The Niemeyer team is focussing on the development of biointerfaces for applications in fundamental cell biology, such as cell signaling and development

Homepage  Details

Random Name

Gerd Ulrich Nienhaus

Advanced optical microscopy: Method development for live microscopy (superresolution, light sheet), data analysis, biophysical research

Homepage  Details

Random Name

Heinz-Peter Schlemmer

Multiparametric and multimodal oncologic imaging

Homepage  Details

Random Name

Lennart Hilbert

The Hilbert lab investigates information processing in dense DNA suspensions, as seen inside the cell nucleus, using microscopy, image analysis, and physical modelling.

Homepage  Details

Random Name

Mark E. Ladd

The Ladd team develops new and optimizes existing biomedical imaging methods (MRI, CT, PET, optical imaging and ultrasound) for diagnostic and therapeutic procedures.

Homepage  Details

Random Name

Matthias Schlesner

The Schlesner team develops and applies methods for data analysis, visualization and integration to explores omics data and address questions in basic and translational cancer research.

Homepage  Details

Random Name

Michael Baumann

The Baumann team integrates omics data, pathological data and radiological imaging for personalized oncology.

Homepage  Details

Random Name

Oliver Jäkel

Our team is developing novel physical and mathematical methods to advance and improve radiation therapy and to analyze and predict it's outcome.

Homepage  Details

Random Name

Uwe Strähle

The Strähle lab investigates the regulatory mechanisms controlling development and regeneration and how these processes are disturbed in various genetic disease models.

Homepage  Details

Training


Overview

The goal of the HIDSS4Health curriculum is that its doctoral researchers should 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). It 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 PhD project, the publication concept, working plans, the cooperation potential with other PhD projects, the planning of the mobility phase, the supervision quality and career perspectives of the doctoral researcher. Although each doctoral researcher will belong to the group of the main supervisor, we recommend an associate membership in the group of a second PI, including an official second affiliation, regular stays in this group (e.g., once a week or a week per month) and integration into lab meetings etc. We anticipate that this will foster a closer integration of data and life science know-how and a deeper understanding of both sides.

Degree

In general, the PhD 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 PhD thesis project will receive a certificate “Data Scientist” including a confirmation about a 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”. It 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. This course 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. 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.
The following lecture course “Advanced Topics in Data Science & Health” has two aims. On the one hand, it provides advanced doctoral researchers and postdocs with an opportunity to gain some experience with that type of teaching format. On the other hand, this course will provide first-year doctoral researchers with an overview about 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 (e.g., 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, e.g. using existing international cooperation networks 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, language) as well as comprehensive support measures regarding internationalization, networking and career orientation. Examples of existing courses are “Introduction to German Academic Culture”, “Publishing in Scientific Journals”, and “Managing Projects”. 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 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 (e.g., by sharing special skills of a doctoral researcher between projects) and to broaden the individual methodological understanding beyond the research ares. Optimal 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.

Apply (Next application round starts in late 2019)


Overall Process

We will perform an annual candidate selection process, announcing 12 open positions in 2019. We will select candidates by 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 interdisciplinarity of the projects. The institutions are committed to increase the percentage of female scientists and encourage female applicants to apply.

Apr 12, 2019 Selection Event in Heidelberg
May 1 - Sep 30, 2019 Start of Doctoral Researchers
Winter semester 2019 Start Courses & Training Program
Late 2019 Start of next application round

Living in Heidelberg / Karlsruhe

Heidelberg and Karlsruhe are attractive cities located in the sunny south-western part of Germany within a travel distance of around 50 kilometers. They are connected with a dense public transport network. More information about living costs can be found at the information pages of the German Academic Exchange Service (DAAD) or in a PDF booklet of the German Federal Ministry for Education and Research.

Requirements

We are looking for excellent graduates holding master degrees, received by July 2019 at the latest, in computer science, mathematics, engineering, physics or related quantitative sciences (e.g., bioinformatics or medical informatics).

Funding

All positions are regular “E13” positions 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 29 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 Affiliate

You are already funded and perform your PhD on a topic that is relevant to the school? Apply for an affilitation!

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 profession and family and therefore offer many options to its 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, in-house childcare facilities and funded places in local childcare facilities offer full-time and part-time child-care places (e.g., 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 leadership positions. These networks are platforms of mutual exchange and aim at fostering the 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 job and family 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.

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