Master Thesis Student – Predictive Maintenance for Connected Fitness Equipment

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EGYM

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

⚽ Sports

🧘 Wellness

☁️ SaaS

Sports • Wellness • SaaS

EGYM is a global leader in fitness technology, dedicated to making workouts smarter, more efficient, and enjoyable for all users. The company's comprehensive fitness ecosystem includes smart workout equipment, digital apps, and services in collaboration with over 200 connected partners. EGYM focuses on providing personalized and interactive workout experiences through advanced technology solutions like Smart Strength and Smart Cardio, as well as management software integration. They serve various types of fitness clubs, from local gyms to large fitness chains, by offering tailored solutions that improve member engagement and operational efficiency. EGYM's innovative approach includes AI-powered training plans and a cloud-based platform that connects gyms, equipment, and members for seamless fitness experiences.

📋 Description

• Data Discovery: You start your journey by conducting a deep dive into our data, mapping telemetry, service tickets, and machine metadata to build a labeled dataset linking sensor signals to historical failure events • Method Benchmarking: You perform a rigorous method benchmarking by comparing classical anomaly detection methods (e.g., Isolation Forest, ARIMA) against modern time-series deep learning approaches (e.g., LSTMs, Transformers) • Proof of Concept: You implement a robust proof of concept, applying your best model to specific machine classes to simulate the alert-to-ticket workflow and measure early breakdown detection • Business Case Quantification: You quantify the concrete business case, analyzing cost reduction potentials, revenue upside opportunities in service contracting, and future product enhancements to capture additional value • Academic Write-up & Publication: You are responsible for the evaluation, interpretation, and documentation of the results in English, preparing both your thesis and a co-authored research paper for publication in reputed journal

🎯 Requirements

• Educational Background: You are about to complete a Master's degree in Computer Science, Data Science, Statistics, Industrial Engineering, or a comparable course of study, and you would like to write your thesis within six months • Technical Expertise: You have strong foundations in time-series modeling and anomaly detection, with proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL. Exposure to Snowflake or Google Cloud is a plus • Bridge Builder Mindset: You love crossing the chasm between theory and practice, translating complex model outputs into tangible business impact at the boundary of research and applied engineering • Working Style & Ambition: You work in a structured, independent manner, bring a high degree of initiative, and have the clear ambition to publish your results in a peer-reviewed paper • Language Proficiency: You have very good English skills (as the thesis and publication will be written entirely in English)

🏖️ Benefits

• Culture : Be part of a modern company culture where your work is designed from day one to be co-authored and published in reputed international journals • Top-Tier Mentorship: Receive direct, high-impact supervision from our VP Customer Support and Service, our AI Transformation Lead, and your academic supervisor • Flexible Workspace : Benefit from a highly flexible setup: while Munich is preferred, remote candidates are welcome, and travel costs to Munich for working sessions will be fully covered • Team Integration: Enjoy a deeply embedded collaboration within EGYM’s AI Transformation and Service Operations teams, combining cutting-edge tech with practical execution • Real-World Data: Gain access to a unique, massive industrial dataset spanning thousands of connected smart fitness machines globally • Fitness : Train in our inhouse gym with all EGYM Smart Strength and Smart Flex machines for free in case you are completing your master thesis in Munich