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This PhD project focuses on strengthening network security for large-scale distributed AI training. As training increasingly spans multiple data centers connected over wide-area networks, it
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gathering knowledge about the diverse physical and geometric properties of objects and dynamic changes in the environment. This involves leveraging rich sensory data—such as vision and touch—encoding
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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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) methods to tackle challenging molecular engineering problems in life sciences and materials design. Situated in the Data Science and AI division, our group advances generative models, molecular simulations
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with expertise in materials characterisation, computer vision, computational modelling, and machine learning. The other PhD positions connected to the project are: PhD Student Position in Generative
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qualifications Marine biogeochemical processes Hydrodynamic processes related to ships, turbulence, or mixing Oceanographic modelling Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary
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Experience with performing laboratory experiments Ability to work with large data sets (> 500 GB) Numerical modelling Main responsibilities Independent research and research training (80% of time) Support for
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. The position is placed in the Division for Computer Networks and Systems and is formally employed by Chalmers University of Technology. Our research spans from theoretical computer science to applied systems
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failures. We offer access to unique experimental data and computational tools developed by our research team for addressing a timely societally relevant problem. Project overview The aim is to unravel
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(advantageous but not required). Familiarity with using digital technologies in construction, like BIM (Building Information Modelling). Main responsibilities Participating in the Graduate School of Civil and