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


In Imaging & Diagnostics, we use 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 need to deal with various challenges including real-time conditions, the quantification of uncertainty and ambiguity in imaging and omics data as well as the development of explicable decision-making.

In Surgery & Intervention 4.0, we focus on the role of data science in robot- and computer-assisted surgery and interventions. This 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 and 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 interactive visualization. Data to be considered includes text data (such as 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).

Projects


Proposed Projects for 2020

Details about the projects for this year's selection round can be found in this document.

Projects 2019

Imaging & Diagnostics

Generating Medical Imaging Reports from 3D Radiological CT Scans using Image Captioning Techniques
Generating Medical Imaging Reports from 3D Radiological CT Scans using Image Captioning Techniques

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

Visualization for Improved Configuration and Analysis in MRI
Visualization for Improved Configuration and Analysis in MRI

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

Generative Probabilistic Models for Predicting Glioblastoma Growth Patterns
Generative Probabilistic Models for Predicting Glioblastoma Growth Patterns

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

Supporting Reuse of Machine Learning Approaches for Neural Network-based Labelling of Microscopy Images
Supporting Reuse of Machine Learning Approaches for Neural Network-based Labelling of Microscopy Images

Fluorescence microscopy has several inherent limitations, which are dictated by basic optical and physical laws as well as compromises arising from... (read more)

Scalable 3D+t Tracking by Graph-based and Deep Learning Methods
Scalable 3D+t Tracking by Graph-based and Deep Learning Methods

Many biomedical imaging devices are able to collect time series of high resolution 3D data (3D+t). A typical task in these... (read more)

Design Principles Underlying Fault Tolerance in Control Systems of Genetically Varying Model Organisms
Design Principles Underlying Fault Tolerance in Control Systems of Genetically Varying Model Organisms

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

Image-Guided Adaptive Radiation Therapy (IGART) Based on Massive Parallelism and Real-Time Scheduling
Image-Guided Adaptive Radiation Therapy (IGART) Based on Massive Parallelism and Real-Time Scheduling

Image-guided adaptive radiation therapy substantially improves state-of-the-art therapy by adapting to movements of the patient. However, its computational complexity prevents a... (read more)

Anomaly Detection Using Unsupervised Learning for Medical Images
Anomaly Detection Using Unsupervised Learning for Medical Images

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

Uncertainty-aware Functional Imaging in Surgery
Uncertainty-aware Functional Imaging in Surgery

Replacing traditional open surgery with minimally-invasive techniques for complicated interventions such as partial tumor resection or anastomosis is one of the... (read more)

Uncertainty Quantification in Radiation Therapy
Uncertainty Quantification in Radiation Therapy

Radiation therapy is one of the cornerstones in modern cancer treatment being applied in 50 % of all patients. It is a... (read more)

Lifelong Machine Learning in Surgical Data Science
Lifelong Machine Learning in Surgical Data Science

Current Machine Learning algorithms, especially Deep Neural Networks (DNNs), have shown to be successful tools in areas particularly relevant for Life... (read more)

Models for Personalized Medicine

Distributed Ledger Technology for Life Science
Distributed Ledger Technology for Life Science

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

Augmenting Physician Workflow to Personalize Care Decisions by Predicting Next Steps and Informational Needs in (Precision) Oncology
Augmenting Physician Workflow to Personalize Care Decisions by Predicting Next Steps and Informational Needs in (Precision) Oncology

With the possibility of whole genomic sequencing for oncologic patients, many processes in their treatment have to be adapted. Physicians in... (read more)

Integrative Analysis of Patient Multi-omics Data
Integrative Analysis of Patient Multi-omics Data

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

Ralf Mikut

Ralf Mikut

Karlsruher Institute of Technology

Homepage

Klaus Maier-Hein

Klaus Maier-Hein

German Cancer Research Center

Homepage

Michael Gertz

Michael Gertz

Heidelberg University

Homepage

Elected Members

Ines Reinartz

Ines Reinartz(Postdoc)

Karlsruher Institute of Technology

Homepage

Paula Breitling

Paula Breitling(Doctoral researcher)

Karlsruher Institute of Technology

Homepage

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

Ines Reinartz

Ines Reinartz

Karlsruher Institute of Technology

Jens Petersen

Jens Petersen

German Cancer Research Center

Kathrin Brunk

Kathrin Brunk

German Cancer Research Center

Administration

Stefanie Strzysch

Stefanie Strzysch

German Cancer Research Center

Michaela Gelz

Michaela Gelz

German Cancer Research Center

Janina Dunning

Janina Dunning

German Cancer Research Center

Jasmin Thiel

Jasmin Thiel

Karlsruher Institute of Technology

Data Science Groups


Achim Streit

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

Ali Sunyaev

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

Alin Albu-Schäffer

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

Anne Koziolek

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

Benedikt Brors

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

Carsten Dachsbacher

Carsten Dachsbacher

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

Homepage Details

Carsten Rother

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

Dorothea Wagner

Dorothea Wagner

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

Homepage Details

Filip Sadlo

Filip Sadlo

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

Homepage Details

Franziska Mathis-Ullrich

Franziska Mathis-Ullrich

The Mathis-Ullrich team aims to increase efficiency and patient-safety during medical procedures through innovative robotic instruments, as well as robot-assistance and machine learning for surgical intervention.

Homepage Details

Holger Fröning

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

Klaus Maier-Hein

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

Lena Maier-Hein

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

Martin Frank

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

Michael Beigl

Michael Beigl

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

Homepage Details

Michael Gertz

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

Oliver Stegle

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

Peter Sanders

Peter Sanders

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

Homepage Details

Rainer Stiefelhagen

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

Ralf Mikut

Ralf Mikut

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

Homepage Details

Robert Strzodka

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

Tamim Asfour

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

Vincent Heuveline

Vincent Heuveline

The EMCL focuses on uncertainty quantification (UQ) in scientific computing, high performance and data intensive computing, with main application focuses in medical engineering.

Homepage Details

Life Science Groups


Alexander Schug

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

Christof M. Niemeyer

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

Daniel Durstewitz

Daniel Durstewitz

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.

Homepage Details

Daniel Hübschmann

Daniel Hübschmann

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.

Homepage Details

Frank Ückert

Frank Ückert

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

Homepage Details

Gerd Ulrich Nienhaus

Gerd Ulrich Nienhaus

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

Homepage Details

Hannes Kenngott

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

Heinz-Peter Schlemmer

Heinz-Peter Schlemmer

Multiparametric and multimodal oncologic imaging

Homepage Details

Jens Kleesiek

Jens Kleesiek

The Kleesiek team develops machine learning algorithms for radiological and other clinical data. An emphasis lies on the translation of these computational methods into clinical settings.

Homepage Details

Jürgen Debus

Jürgen Debus

Our goal is to establish precision medicine in Radiation Oncology with innovative high-precision protocols with ions and photons and molecular stratification of patients in over 40 trials.

Homepage Details

Lennart Hilbert

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

Mark E. Ladd

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

Martin Wagner

Martin Wagner

The Wagner team investigates methods of artificial intelligence and cognitive robotics to improve surgical treatment with a strong focus on clinical translation.

Homepage Details

Matthias Schlesner

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

Michael Baumann

Michael Baumann

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

Homepage Details

Oliver Jäkel

Oliver Jäkel

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

Homepage Details

Uwe Strähle

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 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

This year, the lecture Data Science & Health is 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 TBA
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:

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: November 3rd/4th 2020, Heidelberg, DKFZ.

Internal

HIDSS4Health Retreat

On the HIDSS4Health Retreat all HIDSS4Health doctoral researchers present and discuss their projects and potentials for collaborations.

Next: September 23-24 2020, Bad Liebenzell (internal HIDSS4Health Event)

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


List of relevant events for the school hosted by the school itself or by partner institutions.

Upcoming Events

8
4
2020

HIDSS4Health Selection Event

  • 1 day
  • Online

Second round internal selection event for invited candidates (online format).

16
5
2020

Healthcare Hackathon in Mainz

  • 3 days
  • Mainz

More information here.

26
5
2020

HIDSS4Health Retreat

  • 2 days
  • Bad Liebenzell

First retreat for doctoral researchers of the school.

29
5
2020

Bioinformatics Career Day

  • 1 day
  • Heidelberg

More information will be available soon on www.dkfz.de/careerday.

28
6
2020

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).

29
6
2020

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.

12
7
2020

ICVSS 2020 (International Computer Vision Summer School)

  • 7 days
  • Sicily

More information about the school and the application procedure can be found here.

3
8
2020

Machine Learning Summer School-Indonesia

  • 7 days
  • Indonesia

More information about the school and the application procedure can be found here.

23
9
2020

Helmholtz Virtual Data Science Career Day

23
9
2020

HIDSS4Health Retreat

  • 2 days
  • Bad Liebenzell

Retreat for doctoral researchers of the school.

13
10
2020

Publishing in Scientific Journals

27
10
2020

AI in Healthcare Symposium

The symposium brings together researchers from across the globe to address the promises and challenges of artificial intelligence in healthcare.

3
11
2020

Presentation Skills Course

5
11
2020

HIDA Datathon for Grand Challenges on Climate Change

11
1
2021

Mediterranean Machine Learning summer 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