Research at the interface between AI, high-performance computing, and big data analysis holds immense potential for transformative advancements in various fields. By combining AI algorithms, powerful computing resources, and the ability to process massive amounts of data, researchers at ZIB can unlock new insights and solutions to complex problems. This multidisciplinary research area enables the development of efficient algorithms at the interface between machine learning, simulation, and optimization. Moreover, the synergy between AI and high-performance computing allows for the training and deployment of deep learning models at scale, enabling breakthroughs in areas such as digital health, image processing, or explainability. Additionally, the integration of big data analysis techniques provides the means to extract valuable information from diverse data sources, leading to improved predictions and enhanced data-driven decision-making.

HPC and Machine Learning for Drug Discovery

HPC and ML for Drug Discovery

High-performance computers are required to simulate complex systems that allow to study the behavior of molecules under real conditions. This project combines artificial...

HPC and ML for Drug Discovery
Pareto-ML-Optimization-Cycle

AA1-19 Drug Candidates as Pareto Optima in Chemical Space

The search for novel drug candidates that, at the same time, act with high efficacy, comply with defined chemical properties, and also show low off-target effects can be...

AA1-19 Drug Candidates as Pareto Optima in Chemical Space
ZIB

FAN

 Scholarly communication is facing a radical change with the introduction of Artificial Intelligence (AI) methods. A disruptive change of the whole system seems possible...

FAN
MODAL

Research Campus MODAL

The Forschungscampus ("Research Campus") MODAL is a platform for a public-private innovation partnership established by ZIB and Freie Universität Berlin together with...

Research Campus MODAL