Reinhold Schneider is affiliated with the Technical University of Berlin in Germany. Their research intersects multiple fields including Engineering, Mathematics, and Physics and Astronomy. The scientist's work spans several specialized subfields such as Statistical and Nonlinear Physics, Computational Mechanics, Computational Mathematics, Statistics, Probability and Uncertainty, and Artificial Intelligence.
The scientific contributions of Reinhold Schneider are documented through a consistent publication record across various academic venues. Frequent publication outlets include:
Reinhold Schneider's recent papers include the following titles:
Their frequent coauthors include Mathias Oster, Leon Sallandt, Martin Eigel, Philipp Trunschke, and Gitta Kutyniok. The scientist has collaborated most often with Mathias Oster, followed by Leon Sallandt and Martin Eigel.
Reinhold Schneider's research mainly concentrates on topics related to Model Reduction and Neural Networks, Tensor decomposition and applications, Advanced Numerical Methods in Computational Mathematics, Probabilistic and Robust Engineering Design, Sparse and Compressive Sensing Techniques, Mathematical Approximation and Integration, and Neural Networks and Applications.
This profile reflects a research trajectory marked by engagement with computational and numerical methods applied to complex problems in mathematics and engineering, notably involving neural networks, tensor formats, and probabilistic approaches.
Luigi Genovese;Alexey Neelov;Stefan Goedecker;Thierry Deutsch
Sebastian Holtz;Thorsten Rohwedder;Reinhold Schneider
Szilard Szalay;Max Pfeffer;Valentin Murg;Gergely Barcza
Sebastian Holtz;Thorsten Rohwedder;Reinhold Schneider
Wolfgang Dahmen;S. Prössdorf;Reinhold Schneider
Helmut Harbrecht;Michael Peters;Reinhold Schneider
Wolfgang Dahmen;Reinhold Schneider
Reinhold Schneider
Wolfgang Dahmen;Reinhold Schneider
Gitta Kutyniok;Gitta Kutyniok;Philipp Petersen;Mones Raslan;Reinhold Schneider
Christian Lubich;Thorsten Rohwedder;Reinhold Schneider;Bart Vandereycken
Wolfgang Dahmen;Helmut Harbrecht;Reinhold Schneider
Holger Rauhut;Reinhold Schneider;Zeljka Stojanac;Zeljka Stojanac
Thorsten Rohwedder;Reinhold Schneider
Reinhold Schneider;André Uschmajew
W. Dahmen;S. Prössdorf;R. Schneider
Helmut Harbrecht;Reinhold Schneider;Christoph Schwab
Tobias von Petersdorff;Christoph Schwab;Reinhold Schneider
Markus Bachmayr;Reinhold Schneider;André Uschmajew
Stephan Dahlke;Wolfgang Dahmen;Reinhard Hochmuth;Reinhold Schneider
If you think any of the details on this page are incorrect, let us know.
For students interested in Mathematics, exploring related online degrees can open doors to diverse career opportunities. Business-focused programs such as the easiest MBA offer an approachable pathway for those looking to blend quantitative skills with leadership and management expertise.
If convenience and flexibility are priorities, considering the easiest MBA programs could be a strategic choice. These programs often combine data analytics, finance, and strategic thinking, enabling math graduates to pivot into business roles effectively.
For advanced professionals aiming for higher leadership roles, online DBA programs provide a practical route to deepen expertise in business administration with an emphasis on research and application.
Alternatively, pursuing one of the best online masters in finance can leverage mathematical skills in financial modeling, risk analysis, and investment strategies, areas currently in high demand within the financial sector.
US Forest Service
University of Illinois at Urbana-Champaign
University of Bern
Duke University
Physikalisch-Meteorologisches Observatorium Davos, World Radiation Center
Universität Hamburg
Stanford University
Memorial University of Newfoundland
Federal University of Paraíba
Woods Hole Oceanographic Institution
United States Department of the Army
National Institutes of Health
City University of Hong Kong
University of Ljubljana
University of North Carolina at Chapel Hill
Smithsonian Tropical Research Institute
