25 affective-computing-emotion Postdoctoral positions at Utrecht University in Netherlands
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, Information & Computing Sciences, Physics, Chemistry and Mathematics. Together, we work on excellent research and inspiring education. We do so, driven by curiosity and supported by outstanding infrastructure
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, to eventually capture them in physical-chemical models that predict the impact of acidification on marine P cycling. The PHOSFLUX project is a collaboration between the NIOZ (dr. Peter Kraal) and Utrecht
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interest in societal impact. You have: Must have criteria: a PhD in computer science, artificial intelligence, data science, computational social science, urban analytics, geoinformatics, or a related field
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(UTC) Country Netherlands Type of Contract Temporary Job Status Not Applicable Hours Per Week 40.0 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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should: Be committed to research excellence and impact-oriented research, in line with Utrecht University's mission; Actively engage with the CARDIMED consortium and the Utrecht University CARDIMED team
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qualities We are looking for an enthusiastic and collaborative researcher with a strong technical background and an interest in societal impact. You have: a PhD in GIScience, geoinformatics, spatial data
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-year postdoc project, funded by an NWO Open Competition Large grant, will be conducted in the Developmental Psychology Program at Utrecht University. Your job Today’s young people are coming of age in a
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Biological sciences » Biology Computer science » Informatics Computer science » Programming Researcher Profile Recognised Researcher (R2) Application Deadline 16 Oct 2026 - 21:59 (UTC) Country Netherlands Type
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operational design. Design reproducible scenario and sensitivity analyses to quantify model uncertainty and identify the parameters that most strongly affect storage performance. Assess sustainable infiltration
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measurements and/or Computational Fluid Dynamics (CFD) simulations. Specifically, you will assess where simplified assumptions in the model fall short. You will then train a machine learning model (such as PySR