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verifiability for AI systems, based at the Department of Computer and Information Science. These positions are funded by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Wallenberg AI
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cancer. The goal will be to find genetic prediction models to be able to predict which childhood cancer patients have a high or low risk of toxicity in childhood cancer. Preliminary the doctoral project
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application! We are looking for up to two PhD students in trustworthy machine learning, with a particular focus on cybersecurity, privacy, and verifiability for AI systems, based at the Department of Computer
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) analysing the effects of different trade-offs between timber production and biodiversity under the influence of climate change, and 3) developing optimisation models based on heuristic and AI-based methods
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of Systems and Control, we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms
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. -Machine learning code generation for autonomous translation of payload data semantics. -Dictionary learning and algorithms for translation between major data modeling languages. -Model-based System
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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/thesis: Industry-/collaboration PhD student in optimized off-road driving in forests Research subject: Soil science Description: We are looking for an industry/collaboration-based PhD student to develop a
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aims to build predictive and physical binding models of protein – DNA interactions using high-throughput and quantitative biochemical binding data across hundreds of thousands of sequence variants
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based integration of software-defined CPS (Cyber-Physical Systems) and IoT devices. -Replication and software updates during runtime for mission-critical devices and systems. -Model based engineering