Forschungsprojekte

Project duration: 2025-01-01 - 2027-06-30

Campus Mobility Rosenheim Traunstein

Sub-project lead THRO: Prof. Dr. Wibke Michalk, Dr. Bettina Ruth-Anneser

Übergreifend digitale Technologien Digitale Transformation Klimaschutz Mobilität Mobilitätsmanagement Nachhaltigkeit

Project duration: 2024-04-01 - 2026-03-31

digi.peer - Digital peer mentoring in the context of a literature and research workshop in the Bachelor's degree program in Social Work

In the digital teaching project digi.peer, students in peer groups are to learn how to use digital future skills when writing their academic theses.

Project lead THRO: Prof. Dr. Edeltraud Botzum

Übergreifend Digitale Transformation Lehre

Project duration: 2024-04-01 - 2026-03-31

Freiraum 2023_Feedback based on Analytics of Teaching and Studying meets Individual Coaching (FANTASTIC)

The FANTASTIC project combines AI-generated feedback with the pedagogy of learning and teaching and the didactics as three essential levels of support in order to improve the learning behavior and thus the and thus the academic success of students in a forward-looking way.

Project lead THRO: Prof. Dr. Elmar Junker, Prof. Dr. Birgit Naumer

Übergreifend Digitale Transformation Lehre Pädagogik

Project duration: 2023-10-09 - 2023-12-31

Stärkung der praxisorientierten (Holzbau)-Lehre an der Technischen Hochschule Rosenheim im Rahmen der Holzbauinitiative Bayern

Project lead THRO: Dr. Sebastian Hirschmüller

Übergreifend Bautechnik Bioökonomie Holzbau Holztechnik Lehre

Project duration: 2023-10-01 - 2026-09-30

Spritzgießen von Zellulosefasern - Entwicklung des Herstellungsprozesses für Anwendungsbereiche von Papier für werkzeugfallende technische Bauteile

Overall lead for THRO input THRO: Prof. Dr. Manuela List, Prof. Dipl.-Ing. Martin Würtele

Übergreifend biologische Abbaubarkeit Bioökonomie Nachhaltigkeit Prozesstechnik Spritzgussverfahren Zellulosefasern

Project duration: 2023-05-01 - 2026-04-30

KICK-PV: AI-based characterization and classification of PV systems for predictive maintenance

Improvement the precision of remote maintenance of photovoltaic systems using AI methods. Fault patterns are to be assessed according to and cause in order to prevent damage at an early stage.

Sub-project lead THRO: Prof. Dr. Achim Schulze

Übergreifend Energieeffizienz Energietechnik KI Künstliche Intelligenz Photovoltaik