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Deadline 18 Oct 2026 - 23:59 (Europe/London) Country Portugal Type of Contract Not Applicable Job Status Not Applicable Is the job funded through the EU Research Framework Programme? European Union / Next
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received a doctoral degree in the one of the relevant fields. Preferred Skills: Awareness of the biological relevancy of and applications for developed computational models and workflows Experience analyzing
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of the interactions between building stocks, heat and cooling demand, and electricity distribution networks. They will develop new computational approaches, contribute to the expansion of existing modelling tools, and
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countries in Southeast Asia, that are supported by the EOS Centre for Geohazard Observations, as well as a Remote Sensing Lab (EOS-RS). Computational resources are available through a dedicated cluster and
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Research Fellow (see separate announcement ) with a background in computational thermal physics, who will use the experimental data to develop a computational model / digital twin of the human body
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of Excellence to improve outcomes in HCC. You will oversee the preclinical models of liver cancer and administrative activities, in line with the overall programme. This implies perform and support in vivo work
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. Dairy Forage Research Center (DFRC) has an opportunity for a Post Doctoral Research Fellow to engage in a process and statistical modeling research opportunity focused on improving nutrient use efficiency
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models Identify the weaknesses of existing models for the targeted project applications. Propose and develop state-of-the-art computer vision techniques to overcome existing challenges Train and evaluate
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Biotechnology and Bioprocesses, Membrane Technology, Chemicals and Materials, Resource Recovery, and Modeling & Artificial Intelligence. By harnessing its cross-cutting, interdisciplinary, and transformative
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prediction of regional climate and extremes using hybrid physics-AI models. About the project/work tasks There is a growing need for subseasonal-to-seasonal (S2S; 2 weeks to 12 months) predictions of regional