Diagnostic Tool for Rare Diseases: Mathematics for Medicine How our researchers use mathematical models to detect rare diseases more quickly. © Patrick Pollmeier / Hochschule Bielefeld (HSBI) Diagnostic Tool for Rare Diseases: Mathematics for Medicine How Our Researchers Use Mathematical Models to Detect Rare Diseases More Quickly © Patrick Pollmeier / Hochschule Bielefeld (HSBI) Diagnostic Tool for Rare Diseases: Mathematics for Medicine How Our Researchers Use Mathematical Models to Detect Rare Diseases More Quickly Diagnostic Tool for Rare Diseases: Mathematics for Medicine How our researchers use mathematical models to detect rare diseases more quickly. © Patrick Pollmeier / Hochschule Bielefeld (HSBI) Diagnostic Tool for Rare Diseases: Mathematics for Medicine How Our Researchers Use Mathematical Models to Detect Rare Diseases More Quickly © Patrick Pollmeier / Hochschule Bielefeld (HSBI) Diagnostic Tool for Rare Diseases: Mathematics for Medicine How Our Researchers Use Mathematical Models to Detect Rare Diseases More Quickly Diagnostic Tool for Rare Diseases: Mathematics for Medicine How our researchers use mathematical models to detect rare diseases more quickly. © Patrick Pollmeier / Hochschule Bielefeld (HSBI)

Kaiserslautern. Detecting Rare Diseases Faster Thanks to Mathematical Models

Rare diseases affect more people than one might think, but they are often difficult to diagnose. On average, it takes years for patients to receive a diagnosis. In this project, our researchers are working with the pharmaceutical company Chiesi to develop a mathematical support tool that will help doctors identify rare diseases more quickly and test for them in a targeted manner.

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Trondheim. Designing Fairer Futures: How CHAIN Uses Data to Reduce Global Health Inequality

By Terje Andreas Eikemo, Sara Martino and Andrea Riebler Around the world, education and health shape each other in powerful ways. People with more years

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Trondheim. The Geometry of Data – Why Machine Learning Needs Signatures

By Kurusch Ebrahimi-Fard and Fabian Harang Introduction Think of scribbling a digit on a digital tablet. You might write the number ’3’ slowly, with hesitations,

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Trondheim. The 2nd Ethiopian Study Group with Industry 

By Dietmar Hömberg  (NTNU/WIAS), Zerihun Kinfe Birhanu (Hawassa University) and Anne Kværnø (NTNU) The 2nd Ethiopian Study Group with Industry took place at Addis Ababa University of

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Trondheim. Patterns from instability: How 1D soliton breaks dimensions to find 2D surfaces

By Wei Lian NTNU Norwegian University of Science and Technology In engineering, “instability” is often viewed as a failure—a bridge buckling in the wind or

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Trondheim. Learning Differential Equations via Chen–Fliess series

This article presents an accessible overview of how Chen–Fliess series can be combined with modern learning techniques to model and analyze nonlinear dynamical systems. The

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Trondheim. Affinization of Reduced Basis Methods

by Trond Kvamsdal NTNU Norwegian University of Science and Technology Background Reduced-basis methods (RBMs) offer an efficient framework for computing real-time approximations of parameterized PDEs.

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Workshop: Mathematical methods for explainable AI

A workshop of the ECMI Special Interest group Mathematics for Big Data and Artificial Intelligence Organized by Alessandra Micheletti (Univ. of Milan), Nataša Krejić (Univ.

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Lyngby: In biology, behaviour can be predicted using optimization theory

Biological systems are complex to model mathematically, but optimization theory grounded in Darwin’s evolution offers a solution. This approach predicts organism behaviours under various conditions, aiding in understanding responses to changes like climate shifts. Interdisciplinary collaboration between mathematicians and ecologists is key to advancing this research. Read more in the blog post by Associate Professor Uffe Høgsbro Thygesen from DTU Compute.

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