Master Thesis student (m/f/d): Predictive Maintenance for Connected Fitness at EGYM

Munich, Bavaria, Germany -

Full Time

Start Date

Immediate

Expiry Date

29 Sep, 26

Salary

0.0

Posted On

02 Jul, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Time-series Modeling, Anomaly Detection, Python, SQL, Pandas, Scikit-learn, PyTorch, TensorFlow, Data Discovery, Method Benchmarking, Proof of Concept, Business Case Quantification

Industry

Wellness and Fitness Services

Description

Your daily workout For our team, we are looking for a Master thesis student (m/f/d) who writes their thesis with us on the topic of "Predictive Maintenance for Connected Fitness Equipment” in Munich or remotely. EGYM is a Munich-based fitness technology company operating a global fleet of connected strength and cardio equipment, including the EGYM Smart Strength circle. Each muscle group is served by a single dedicated machine, which means that a failure on one device prevents members from training that muscle group entirely until a technician resolves the issue. Service today is largely reactive. As EGYM scales internationally the reactive model will not scale with it. Does this topic spark your interest, then take the chance and apply now! 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 Your fitness level 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) Your training goal for your first 6 months The equipment we provide 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 Please note that we cannot offer any remuneration for the master thesis Your warm up plan Contact Information Apply now by uploading your documents (CV, short motivation paragraph with 3-5 sentences, start date, certificate of enrollment and any prior work or coursework relevant to time-series or anomaly detection) and specify the job ID #2017. Contact: Lena Sinzinger For further information: https://career.egym.com About us Warning regarding phishing emails: Please be aware that all official EGYM recruitment communication is sent exclusively from jobs@egym.com. If you receive a suspicious message from any other domain, please ignore it and do not share personal information if in doubt. EGYM is a global fitness technology leader, providing fitness and health facilities with intelligent workout solutions. EGYM makes exercising smarter and more efficient with its comprehensive suite of connected gym equipment and digital products that integrate seamlessly with 3rd-party-hard- and software. The result is a fully connected training experience that drives measurable business and health outcomes on and off the training floor. EGYM also offers subscription-based corporate fitness- and wellness solutions built on a combination of gym-access and EGYM fitness programs that directly target costly chronic conditions and boost employee health, leading to higher productivity and well-being. EGYM's global headquarters are in Munich, Germany, with North American offices in Denver, Colorado. EGYM is an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs, regardless of race, gender, religion, sexual orientation, age or any other aspect of an individual's identity.

Responsibilities

The student will develop a predictive maintenance model for connected fitness equipment by analyzing telemetry and service data. Responsibilities include benchmarking anomaly detection methods, implementing a proof of concept, and publishing the results in a research paper.