Martin Nocker, MSc

Professional work experience

09/2025 - present
Teaching & Research Assistant - Management Center Innsbruck
02/2021 - 08/2025
Project Assistant - Management Center Innsbruck
02/2019 - 01/2021
Software Engineer Automation - D. Swarovski KG, Wattens
05/2018 - 12/2018
Master's Student - BMW AG

Education

10/2021 - present
University of Rostock
PhD Student
10/2016 - 12/2018
MSc - Technical University of Munich
Electrical and Computer Engineering
10/2013 - 03/2016
BSc - Leopold-Franzens University Innsbruck
Computer Science

Teaching (faculty internships etc.)

09/2024 - 02/2025
MCI - The Entrepreneurial School
Programming I
02/2024 - present
MCI - The Entrepreneurial School
Smart Systems & Machine Learning

Practice related research and developmental project

03/2024 - 08/2025
Research Assistant - MCI - The Entrepreneurial School
Josef Ressel Center for Security Analysis of IoT-Devices
04/2021 - 09/2023
FFG - Die Österreichische Forschungsförderungsgesellschaft
SMiLE - Secure Machine Learning Applications with Homomorphically Encrypted Data

Peer reviewed journal article

Merkle, F., Weber, D., Schöttle, P., Schlögl, S., & Nocker, M. (2025). Less is More: The Influence of Pruning on the Explainability of CNNs. IEEE Access, 13, 87909–87927. https://doi.org/10.1109/ACCESS.2025.3569575

Supervised bachelor theses

Hetzenauer Kevin (2025): Machbarkeitsanalyse von Federated Learning mit Internet of Things: Vergleich zentralisierter und dezentraler Trainingsansätze
Windisch Jakob (2025): Comparative Analysis of Self-Supervised Learning Algorithms for Effective Data Labeling and Performance Improvements in Computer Vision Models
Brandacher Daniel (2025): Evaluierung wesentlicher Einflussfaktoren bei der Implementierung eines lokalen Open Source Large Language Models
Oberhofer Samuel (2024): Invariance-Based Adversarial Examples: Algorithmic Creation and Human Evaluation for Image Classification
Aster Pirmin (2024): Maschinelle Fehlererkennung im technischen Keramik-3D-Druck
Meusburger Matthias (2024): Analysis of Air Quality Data Using Homomorphic Encryption
Koudelka Paul (2023): Explainable Machine Learning Algorithms While Using Homomorphic Encryption