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Helmholtz Information & Data Science School for Health (HIDSS4Health)

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Helmholtz Information & Data Science School for Health (HIDSS4Health)
Contact:

Dr. Ines Reinartz
apl. Prof. Dr. Ralf Mikut

Links:
Project Group:

Machine Learning for Time Series and Images (ML4TIME)

Funding:

HGF

Partner:

DKFZ, Heidelberg University

Startdate:

2019

Enddate:

2025

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.

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).