
Projects
Hessian scientists of various disciplines are using High Performance Computers for their research.
Hessian scientists of various disciplines are using High Performance Computers for their research.
Displaying 1 - 30 of 37
Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...
In the realm of High-Performance Computing (HPC), the Message Passing Interface (MPI) has established itself as a ...
The project focuses on the cryptanalysis of the Supersingular Isogeny (SSI) path problem, which underlies the isogeny ...
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...
In robotic applications such as autonomous driving and whole-body control, ensuring safety is of utmost importance ...
Robotics platforms can massively benefit from novel Deep Reinforcement Learning approaches. However, robotics have ...
Artificial intelligence is currently developing faster than ever and introduces many different possibilities. Our client ...
Three-dimensional virtual city models play an increasingly important role in various fields, including digital city ...
Hash functions play a vital role in modern computing. Their applications range from database queries, over storage of ...
Many problems in machine learning involve inference from intractable distributions. For example, when learning latent ...
In order to facilitate rapid prototyping and testing in the advanced motorsport industry, we consider the problem of ...
Acoustic keylogging describes a category of side-channel attacks that recover typed keystrokes from audio-streams of ...
Understanding the underlying reward mechanisms of human locomotion and transferring this knowledge to humanoid robots is ...
Lightweight Cryptography is about developing and analyzing algorithms that provably obtain security goals, such as ...
Motion planning is a crucial component of autonomous robot systems. It addresses the problem of finding a feasible ...
Reinforcement Learning (RL) is a promising tool for solving complicated control and decision-making tasks in a data ...
Autonomous robots need to be able to identify and recognize objects in a scene. For this crucial task we need to ...
Analyzing the correlation structure of quantum systems is central to understanding their behaviour. The notion of sector ...
The exaFOAM project (https://exafoam.eu) aims at overcoming the current limitations of Computational Fluid Dynamics (CFD ...
Planning algorithms have shown impressive performance in many domains such as chess and Go. In particular, Monte Carlo ...
In recent years, reinforcement learning and its multi-agent analogue have achieved great success in solving various ...
Massive amounts of newly generated gene expression data have been used to further enhance personalised health ...
The human brain is a large structure (billions of neurons and orders of magnitude more synapses) that is not understood ...
In this project, our primary focus is to validate and benchmark the performance of a measurement-based variational ...
Robotic manipulation stands as a largely unsolved problem despite significant advances in robotics and machine learning ...
Autonomous robotic assembly requires a well-orchestrated sequence of high-level actions and smooth manipulation ...
Neural networks are usually trained with a static architecture. However, the fields of growing and pruning, or ...
With increasing system performance and complexity, it is becoming increasingly crucial to examine the scaling behavior ...
The project focuses on the cryptanalysis of the Computational Supersingular Isogeny (CSSI) problem which underlies the ...
The increase in complex cyber-attacks illustrates the vulnerability of society and information infrastructure. In ...