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Field
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screening performance when optimizing the radiologist’s interpretation. This trial will involve two rounds of screening for 84,000 women and will leverage new knowledge that has been generated in a prior
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perform a prospective trial at the Dutch Breast Cancer Screening Program to determine the impact on screening performance when optimizing the radiologist’s interpretation. This trial will involve two
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that predispose to liver cancer, with the overarching goal of understanding disease mechanisms and developing innovative therapeutic strategies. As part of our team, you will leverage state-of-the-art single-cell
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models for optimized selection of treatment before, and follow-up after EVAR. You will implement and advance multimodal deep learning models combining CT imaging and clinical data, trained on the unique
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profitability. Examples include optimizing production speeds to balance output, equipment deterioration, and energy consumption in manufacturing, or designing dynamic pricing and allocation policies in rental
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of understanding disease mechanisms and developing innovative therapeutic strategies. As part of our team, you will leverage state-of-the-art single-cell, spatial, and organoid technologies in a collaborative and
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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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of HBGI interventions at three European sites; · Monitoring of physical, ecological, and social performance compared to traditional grey infrastructure; · Iterative design optimization in
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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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life-long follow-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