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Intelligence (AI) services in Internet of Things (IoT) networks, where latency and timeliness are paramount. The successful candidate will be responsible for the end-to-end investigation of novel edge-assisted
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for efficient management of complex textile supply and demand chains. The work will be particularly carried out in areas such as sustainable sourcing, logistics and sustainable transport network planning
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fundamental and applied research within communication, networks, control systems, AI, sound, cyber security, and robotics. The department plays an active role in transferring inventions and results
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also connected to the Wallenberg Initiative Materials Science for Sustainability (WISE, https://wise-materials.org ). WISE, funded by the Knut and Alice Wallenberg Foundation, is the largest-ever
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, simplicial complexes, manifold theory, fiber bundles, curvature). Familiarity with topological data analysis—persistent homology, filtrations, stability theorems—is particularly valued. Extensive experience
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-security priorities and challenges. Excellent interpersonal and communication skills, including presenting complex technical ideas to varied audiences. The ability to assess stakeholder needs and develop
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their compatibility with problems from mechanical engineering and materials research that could not be addressed so far due to their high complexity, which prevents approaches that solely rely on classical mechanistic
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science. You are expected to actively participate in the department’s knowledge environments and networks, both internally and externally. You will be part of an international research project focusing
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to support institutional innovation. You will be responsible for the full lifecycle of network hardware and software, from researching and recommending vendor solutions to performing complex troubleshooting
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-generation wireless, geolocation and AI-enabled communication and networking systems. In these roles you will undertake research centered on the intersection of networking, communications, geospatial methods