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Field
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. Expanding their impact in real-world deployments requires addressing the heterogeneity of hardware, data, and resource availability in distributed scenarios. This research will explore novel approaches
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in the Urban Geographies (UG) programme group in the Geography, Planning and International Development department, which studies how resources, risks and political voice are distributed unevenly across
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, risks and political voice are distributed unevenly across urban spaces and populations, analyzing geographies of inequality. You will receive PhD-training through the Amsterdam Institute of Social Science
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suffers from distribution shifts, lack of training data, and poor adaptability to unseen conditions and new problems. Can we combine these two fundamental learning paradigms to synthesize new learning tools
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of how genomic diversity is distributed in forest tree species, how it has evolved, and implications for the future. Methods range from: studying genomic characteristics that drive genome evolution
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scale over molding experiments with natural fiber materials Characterize fiber distribution, adhesion, mechanical properties, and barrier performance Develop and integrate simulation models for material
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of non-model plant species like many forest trees. The McEvoy Lab draws from a variety of new approaches to develop a better understanding of how genomic diversity is distributed in forest tree species
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collaboratively. Desirable skills Experience in training or fine-tuning large-scale models (LLMs, VLMs) in distributed settings. Familiarity with cluster computing environments (e.g., SLURM) and Linux-based
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– Adaptive & Agentic AI. The PhD project focuses on developing robust and reliable machine learning systems that can adapt at test time under real-world distribution shifts. Modern foundation models (e.g
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actively to take advantage of the qualities that diversity and an even gender distribution contribute to our operations. We have chosen media for this recruitment and therefore we reject contact with