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addresses the following central research question: how can we design human-AI collaboration to mitigate biases and foster equitable decision-making? To answer this question, the research will explore areas
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and foster equitable decision-making? To answer this question, the research will explore areas such as the following: Characterizing human-AI collaboration for bias mitigation – Developing a conceptual
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Responsibilities of Financial Institutions for Decarbonization” Project description The financial sector plays a crucial role in directing investments to sustainable causes. So far, sustainable investment has been
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package within the core research program of CropXR. Our goal is to develop computational methods to translate integrated simulation models of plant responses to stress (drought, heat) from a model plant
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scenario analyses, you will help build actionable insights and decision-support tools for responsible AI adoption. You will work closely with academic experts and industrial collaborators, contributing
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, organoids and infant GuMI models, metabolomics and proteomics, the goal is to identify beneficial bacterial metabolites and genes that are responsible for immune training. The project is part of NeoGutChip
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sites, collecting and identifying specimens, and analysing 3D flight trajectories in relation to local environmental cues such as light intensity, wind conditions, and visual structure. A key focus will
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, including trans-differentiation of fibroblasts into neurons (iNeurons), development and screening of antisense oligonucleotides (AONs), live-cell imaging, and transcriptomic analyses. Laboratory work will
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and often hard to target, rendering the development of effective interventions challenging. The endocannabinoid system (ECS) has been emerging as a crucial regulator of stress responses; its disruption
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addition to these core activities, you will perform bioinformatic analyses using your own data and large public data resources and integrate your findings with clinical data (symptoms, EEG, etc). Finally, you will