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George Washington University | Washington, District of Columbia | United States | about 13 hours ago
research advances understanding of climate and environmental change and human interactions with the Earth system. Areas of interest may include biogeography, climatology, water resources, ecosystem and land
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/en/geog/fachrichtungen/menschumwelt/Modelling-Human-Environmental-Interactions/About-us/index.html Job description: This position is financed by the DFG Priority Programme “Sustainability and
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Bio-Inspired Self-Assembling Systems: From Local Interactions to Functional Robots School of Electrical and Electronic Engineering PhD Research Project Self Funded Dr S Miyashita Application
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is for a period of three years which is the nominal length of the PhD programme. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD
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21 Sep 2026 Job Information Organisation/Company International PhD Programme (IPP) Mainz Department Institute of Molecular Biology Research Field Biological sciences Researcher Profile First Stage
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Europe. Opportunity to interact with internationally renowned experts, and with a strong team of postdoctoral researchers and PhD students Informations Only applications submitted through the online
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Computer Science (CS) Department (https://cs.barnard.edu/) has faculty with expertise in Security and Privacy, Natural Language Processing, Computer Graphics, Augmented Reality, AI and Law, Human-Computer
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crosslinking mass spectrometry Function and regulation (DC6 to DC10): computational design of microprotein binders, lncRNA translation in human neurons, evolutionary analysis of new genes, single-cell
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generation of environmental practitioners. For more details and a full list of other PhD projects under this programme, visit: https://ecosolutions.sites.sheffield.ac.uk/ Application Web Page: For more
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from massive datasets. Their ability to capture transferable knowledge offers a unique opportunity to rethink the modeling of physical interactions. The central idea of this PhD is that a model trained