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new skills, expand your knowledge, collaborate across disciplines, and experiment in a friendly environment fully embodying academic freedom across all career levels. The core focus of CML is to perform
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push the boundaries of scientific knowledge by developing and applying high-quality mathematics while offering a wide range of mathematical programs in a friendly and intellectually stimulating
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’ program Foundation Models for Industry (FIND), collaborating with leading Dutch universities, knowledge institutes, and industry partners. The FIND project Foundation for Industry (FIND) – Large AI Models
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oriented towards the development of a domain-specific design automation framework for cryptographic hardware. Your work will focus on identifying the mathematical knowledge and properties to guide hardware
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. Your work will focus on identifying the mathematical knowledge and properties to guide hardware optimizations tailored to different environments. The optimizations range from algebraic optimizations (e.g
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domains. Develop hybrid AI architectures combining symbolic domain knowledge, real-time data streams, and probabilistic inference. Design and evaluate decision support tools capable of interacting with
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willingness to learn one of these languages. Knowledge of Latin is a bonus; You have proven time-management skills; You are a team player and independent thinker; You have the ability to finish the proposed PhD
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, (especially reading), or a willingness to learn one of these languages. Knowledge of Latin is a bonus; You have proven time-management skills; You are a team player and independent thinker; You have the ability
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smart industry. You will be part of a large national ‘Perspectief’ program Foundation Models for Industry (FIND), collaborating with leading Dutch universities, knowledge institutes, and industry partners
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limitations. The field of interpretable machine learning aims to fill this gap by developing interpretable models and algorithms for learning from data. Meanwhile, the field of knowledge discovery and data