Skip to main content
Jump to main menu
Jump to search menu
DE
EN
Accessibility
Internal
Sitemap
Navigation umschalten
Search
Query term
Wildcard: asterisk (*)
Faculty 3 - Mathematics and Computer Science
:
WG Industrial Mathematics
MENU
INFO FOR
Info for
Students
Prospective Students
Continuing Education
Enterprises
Press
QUICKLINKS
Home
Team
Research
Projects
Publications
Events
Home
Team
To list view
Team
Prof. Dr. Dr. h.c. Peter Maaß
Secretariat
PostDocs
Dr. Matthias Beckmann
Dr. Sören Dittmer
Dr. Pascal Fernsel
Dr. Tobias Kluth
Dr. Daniel Otero Baguer
Maximilian Schmidt
PhD Students
Hannes Albers
Clemens Arndt
Alexander Denker
David Erzmann
Louisa Kinzel
Jean Le'Clerc Arrastia
Helge Mohn
Judith Nickel
Sonal Rami
Student assistants
Former members
Guests
Research
To list view
Research
Inverse problems and magnetic particle imaging
Deep learning and inverse problems
Deep learning and digital pathology
Deep learning and industrial applications
Projects
To list view
Projects
Current projects
PY2DLL
AGENS
DELETO - Machine learning in correlative MR and high-throughput NanoCT.
Design-KIT - Artificial intelligence in mechanical component development
DIAMANT
D-MPI - Dynamic Inverse Problems in Magnetic Particle Imaging
Research Training Group π³ - Parameter Identification – Analysis, Algorithms, Applications
HYDAMO - Hybrid data-driven and model-based simulation of complex flow problems in the automotive industry
Inverse problems - Theories, methods and implementations
KIDOHE - AI-supported documentation for midwives
KIWi -Hybrid Parameter Identification with Invertible Networks
MALDISTAR - Study on quality assessment, standardization and reproducibility of MALDI imaging mass spectrometry data
MarDATA - Monitoring a stressed ice shelf - Machine learning algorithms to detect icequakes in 20 years of seismological records at Neumayer station, antarctica
MarDATA - Machine learning in climate modelling
MarDATA - Robust Machine Learning applied to Stratospheric Chemistry Models
ML-X-RAY - Machine learning and X-ray technology for measuring inhomogeneous cable and pipe products
MPI2 - Model-based parameter identification in Magnetic Particle Imaging
ROMSOC - Reduced Order Modelling, Simulation and Optimization of Coupled Systems
SPAplus - Small Data problems in digital pathology
TorchPhysics - A deep learning library for differential equations
Finished projects
Development of a digital staining method as a pathological-histological diagnostic tool based on the MALDI imaging technology
HYPERMATH - Hyperspectral Imaging: Mathematical Methods for Innovations in Medicine and Industry
MaDiPath - Mass Spectrometric Profiling/Grading for routine oncological digital pathology applications
MALDI AMK - 3D-MALDI-Imaging for analysis of proteomic markers and clinical drug distribution
MALDI Imaging Lab - An interdisciplinary core facility for the acquisition and analysis of imaging mass spectrometry data
SceneNet - Mobile Crowd Sourcing Video Scene Reconstruction
Spatially three-dimensionally resolved metabolic analysis for medicine
CRC 747 Micro cold forming - processes, characterisation, optimisation
CRC 1232 From coloured states to evolutionary construction materials
SPP 1324 - Adaptive Wavelet Frame Methods for Operator Equations: Sparse Grids, Vector-Valued Spaces, and Applications to Nonlinear Inverse Parabolic Problems
SPP 1480 - Thermomechanical deformation of complex components by drilling and milling processes
UNLocX - Uncertainty principles versus localization properties, function systems for efficient coding schemes
Publications
Events
To list view
Events
2021 - KoMSO Academy
2020 - Code Sprint
2019 - Deep Learning and Inverse Problems
2019 - ROMSOC Training Course - Introduction to Deep Learning
Search
Query term
Wildcard: asterisk (*)
You are here:
WG Industrial Mathematics
Details
Next funding period of the RTG has been approved!
We are pleased to announce that the DFG has approved another funding period (until 2025) for the π³ Research Training Group. A total of 25 Research Training Groups (10 of them new) will be funded.
© ZeTeM
Updated by:
RTG Web Team
RSS
Print page
Scroll to the bottom of the page
Scroll to the top of the page
