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standard fine-tuning. Key research objectives include: Developing efficient algorithms: exploring and designing training strategies (e.g., supervised finetuning, reinforcement learning, or new alignment
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construction machinery to improve efficiency, adaptability, and safety under varying operating conditions. The work will involve designing and prototyping intelligent control algorithms, developing runtime
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advanced AI algorithms to optimize and understand the optical properties of light-trapping surfaces. (more information can be found in the following News post ). You will work closely with colleagues both
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and nanostructuring, all guided by advanced AI algorithms to optimize and understand the optical properties of light-trapping surfaces. (more information can be found in the following News post ). You
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algorithms. Graph Neural Networks. The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics or another field
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modeling tools and HDL simulators to validate functionality. Collaborate closely with algorithm designers to co-optimize architecture. Publish results in high-impact journals and conferences. Qualifications
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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: Algorithms for the analysis of orthomosaics with the purpose of locating unwanted / weed plants Software for automating processing of large data sets (quality control, orthomosaic generation, and analysis
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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thus including sensing systems, tool condition features selection, algorithms for automated signal preprocessing, feature extraction and decision making based on ML and AI. An integral part of