Prof. Dr. Tim Dahmen

Room

G2 1.13

Telephone

+49 7361 576-4184

Email Address

tim.dahmen[at]hs-aalen.de

Resume
Publications

AI workshop for SMEs

Scientific Scores

2004 - 2015 Education

2004 - today Professional Experience

Aalen University

DFKI GmbH, since 01/2014 leading Team ”Computational 3D Imaging”

Saarland University, Beginning of graduation

IMC AG

Xaitment GmbH, management of development projects for performance critical artificial intelligence (AI) components for computer games and simulations.

SAP Deutschland AG, During this employment assignments (among others) for: Hyundai Kia Motor Company, Seoul, South Korea; AMECO Beijing, China; Collectum AB, Stockholm, Sveden; Centrica plc, London, UK; Acceptance to top-talent carreer path of SAP Germany.

InTrace GmbH, development of performance critical realtime applications in computer graphics.

Professional Activities

Teaching & Supervision

Other Qualifications

Fei Chen, Sunita Saha, Manuela Schuler, Philipp Slusallek, Tim Dahmen ISSN: 2475-1421,

Further information

Christian Wolff, Elke Warmerdam, Tim Dahmen, Tim Pohlemann, Philipp Slusallek, Bergita Ganse ISSN: 1424-8220,

Further information

Tim Dahmen, Markus Kronenberger, Niklas Rottmayer, Katja Schladitz, Claudia Redenbach

Christian Wolff, Elke Warmerdam, Tim Dahmen, Tim Pohlemann, Philipp Slusallek, Bergita Ganse ISSN: 0966-6362,

Further information

Tim Dahmen, Manar Samri, Jonathan Thiemecke, Eva Prinz, René Hensel, Eduard Arzt ISSN: 1369-7021,

Further information

Tim Dahmen, F. Chen, P. Slusallek, M. Müller

Further information

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Prof. Dr. Tim Dahmen

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Dr.-Ing. in Computer Science

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W3 Professor for Machine Learning and Computer Vision

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Publications

im2im

New Parameters Based on Ground Reaction Forces for Monitoring Rehabilitation Following Tibial Fractures and Assessment of Heavily Altered Gait

A Neural Model for High-Performance Scanning Electron Microscopy Image Simulation of Porous Materials

Novel ground reaction force-based parameters for monitoring rehabilitation in tibial fractures

Predicting the adhesion strength of micropatterned surfaces using supervised machine learning

Towards Visual Programming Image Processing in Jupyter Notebooks