Vorlesung: Advanced Biomedical Systems Modeling and Data Science - Details

Vorlesung: Advanced Biomedical Systems Modeling and Data Science - Details

Allgemeine Informationen

Veranstaltungsname Vorlesung: Advanced Biomedical Systems Modeling and Data Science
Veranstaltungsnummer INF-3815
Semester SS 2026
Aktuelle Anzahl der Teilnehmenden 9
Heimateinrichtung Lehrstuhl für Modellierung und Simulation biologischer Prozesse - Prof. Dr. Andreas Raue
Beteiligte Einrichtungen Fakultät für Angewandte Informatik, Institut für Physik, Medizinische Fakultät
Veranstaltungstyp Vorlesung in der Kategorie Lehre
Erster Termin Donnerstag, 23.04.26, 14:00 - 15:30 Uhr Gebäude N Raum 2013
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile Ja
Hauptunterrichtssprache englisch

Räume und Zeiten

Gebäude N Raum 2013

  • Donnerstag, 14:00 - 15:30, Wöchentlich (ab dem 23.04.26)

Modulzuordnungen

Kommentar/Beschreibung

This interdisciplinary course provides advanced-level knowledge of computational techniques in systems modeling and data science applied to complex biomedical challenges. Students will expand their expertise in mathematical modeling, machine learning, and biological data analysis. The course emphasizes both theoretical understanding and practical application through project-based learning. A solid background in biology, mathematics (calculus, linear algebra, differential equations), and programming (Python) is required. The goal of this course is to prepare students for a career path as scientists in biomedical, biotechnology or pharmaceutical industry, or for continuing their academic training and research by acquiring a PhD degree.

Objectives:
• Deepen understanding of systems modeling and data science principles.
• Develop expertise in handling complex datasets and applying advanced models and methods.
• Enhance proficiency in interpreting and presenting biomedical data using advanced models.
• Strengthen project design and critical evaluation skills for scientific research.

Contents:
Part 1: Data Science
• Omics Data Integration
• Statistical Theory & Applications
• Machine Learning Techniques
• Artificial Ground Truth
Part 2: Systems Modeling
• Dynamical Systems & Modeling Concepts
• Model Development & Analysis
• Experimental Design
• Advanced Model Classes