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Postdoc in computational social choice for large-scale deliberation (1.0 FTE) (V25.0203) « Back to the overview Job description A 2-year postdoc position in computational social choice for digital
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uncovering companion biomarkers for precision treatment using cutting-edge bioinformatics and proteomics approaches. You will analyze bulk and clonal protein expression data from large melanoma cohorts
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bulk and clonal protein expression data from large melanoma cohorts, integrate molecular, histological, and clinical data through machine learning (ML)/AI-assisted methodologies. Your expertise in ML
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expertise in data science, with hands-on experience in techniques such as machine learning, reinforcement learning, and simulations, and in handling large-scale, within-person, or real-time datasets; A strong
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a large interdisciplinary consortium of digital democracy researchers as well as several societal partners. The successful applicant is expected to carry out research on the algorithmic foundations
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image analysis (segmentation, statistical feature extraction etc.) The project is part of a larger collaboration involving the Faculties of the University of Twente and a large industrial company
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traditional large-sample approaches to study fewer individuals in unprecedented detail, leveraging an already collected rich dataset to better understand individual brain differences in autism. What are you
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‘RobotLab’, large numbers of experiments can be carried out, yielding large datasets on properties of molecular systems. With this data, ML models are trained to predict the properties of molecular systems
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’ sending and receiving regions. It uses big data and agent-based modelling to analyse the impact of those changing patterns of spatial mobility on EU regions and envisage policies to counterbalance
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H (FH) protects human cells. The group has a large portfolio of in-house generated antibodies against human complement proteins which are used to develop assays and tools to further study this system