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trial focused on optimizing mammography interpretation with AI assistance. In this project, we will perform a prospective trial at the Dutch Breast Cancer Screening Program to determine the impact on
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in Kenya. The insights gained from your research will contribute directly to optimizing the deployment of spatial repellents to reduce malaria transmission. You will be part of a diverse and
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, rental platforms, and production systems—where decision-making must balance conflicting objectives, leverage real-time data, and ultimately support sustainable profitability. Examples include optimizing
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developing morphing surfaces enabled by Shape Memory Alloys (SMAs). These adaptive winglets are designed to optimize aerodynamic performance by responding to temperature variations and incorporating active
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alternatives such as HBI, DRI, and scrap. These changes introduce new challenges in maintaining furnace stability, gas flow, and productivity. This PhD project focuses on developing and applying advanced
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-up. In the ZonMW-funded AI for EVAR project, we develop multi-modal models for optimized selection of treatment before, and follow-up after EVAR. You will implement and advance multimodal deep learning
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group in the Department of Cognitive Robotics. You will be supervised by associate professor Luka Peternel and associate professor Jens Kober, and work with MSc students and PhD candidates in
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background and interests. Candidate Profile We are looking for candidates who meet the following criteria: PhD in a related discipline. Expertise in one of the following areas: Single-cell and spatial
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-optimal production outcomes. Deviations from normal behavior have diverse causes, ranging from misplacement, heterogeneity in material, to fatigue. Locating such issues in the production line can be
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-optimal production outcomes. Deviations from normal behavior have diverse causes, ranging from misplacement, heterogeneity in material, to fatigue. Locating such issues in the production line can be