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Thesis (master): confidential edge-cloud based semantic segmentation for visual tool condition monitoring

München
Siemens
Master
Inserat online seit: Veröffentlicht vor 10 Std.
Beschreibung

Mode of Employment: LimitedStart your career with an exciting thesis As part of our dynamic team, you will work on pioneering projects and can put your theoretical knowledge directly into practice.Our R&D department develops technologies for the future of predictive maintenance and smart manufacturing on an industrial scale. In our work, we create technology with purpose. Therefore, we innovate in the field of plant and control technology, process monitoring, and industrial data management.What you'll love about usExciting research and development projects that put your theoretical knowledge into practiceIndividual supervision and support from experienced experts in your fieldAccess to the latest technologies, laboratories, and resourcesDiverse opportunities to contribute your ideas and actively shape the projectsExcellent career opportunities through contact with potential employersYou'll make an impact byThe focus of your thesis will be on designing and implementing a scalable and secure edge-cloud architecture for semantic segmentation of visual data in tool condition monitoring.You will investigate how sensitive image data from manufacturing processes can be processed locally (on edge devices) and selectively shared with cloud platforms while ensuring confidentiality and performance.Your work will include the development of a prototype system that integrates semantic segmentation models with edge computing frameworks and secure data pipelines.You will evaluate different strategies for data anonymization, encryption, and access control to enable trustworthy collaboration between manufacturing units and stakeholders.The system will be validated using real-world image data from machining processes and benchmarked for latency, accuracy, and security.A user-friendly interface will be developed to allow operators to control what data is shared, ensuring transparency and compliance with data governance policies.Your defining qualitiesEducation: You are currently enrolled in a technical or scientific degree such as computer science, electrical engineering, or information systems.Experience & Skills:You have solid programming experience (ideally in Python or JavaScript) and are familiar with machine learning frameworks (e.g., PyTorch, TensorFlow).Knowledge of edge computing, cloud platforms (e.g., Azure, AWS), and container technologies (e.g., Docker, Kubernetes) is highly beneficial.Experience with computer vision, especially semantic segmentation, is a strong plus.Ways of working: You enjoy scientific work and are characterized by a structured and independent way of working.Languages: Very good written and oral English or German skills are required.You are much more than your qualifications, and we believe in the potential of every single candidate. We look forward to getting to know youYour individual personality and perspective are important to us. We create a working environment that reflects the diversity of the society and support you in your personal and professional development. Let's get to know your authentic personality and create a better future together with us. As an equal-opportunity employer we are happy to consider applications from individuals with disabilities.About usAs a leading technology company, Siemens offers you excellent opportunities to write your bachelor's or master's thesis in an innovative and practical environment. Are you nearing the end of your studies and looking for an interesting topic for your bachelor's or master's thesis? Then you are in the right place with us – if you would like to find out more about jobs & careers at Siemens.FAQ – if you need further information on the application process.If you have more questions please contact: sagthesis

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Home > Stellenangebote > Erziehung Jobs > Master Jobs > Master Jobs in München > Thesis (Master): Confidential Edge-Cloud based Semantic Segmentation for Visual Tool Condition Monitoring

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