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Application deadline: 15 May 2025 Apply now Are you fascinated by the complex ways our social environments shape health? Do you want to develop cutting-edge methods to measure social networks at a population
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. This position offers you rich development opportunities. You will be part of the Hybrid Intelligence consortium external link , a network of excellence of universities and institutes in the Netherlands focused on
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visualisation. Additionally, this position will equip you with important academic skills such as teaching, effective scientific communication (written and oral), and building collaboration networks. We want
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. The group is world-leading in modelling of the ice sheet surface including firn, and maintains a dedicated network of automatic weather stations. Currently, our research group has 6 staff members, 8 Postdocs
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Master's programmes Pharmacy . Your PhD position is part of the Academic Research Network HTA, a collaboration between the National Health Care Institute (Zorginstituut Nederland (ZIN)), Erasmus University
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-host interactions. Collaborative Network: You work closely with multidisciplinary teams, blending insights from chemical biology, virology, and national collaborators at the Department of ViroScience
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interdependencies between inspection items using probabilistic graphical models like Bayesian networks. These models aim to support interactive inspections by prioritizing items dynamically, combining data-driven
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will develop numerical models to simulate fluid injection-induced fracture and flow behaviour, as well as permeability evolution in fracture systems, utilising fracture networks mapped from geothermal
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analysis of secular and/or non-religious imagery in transnational Iranian digital culture; network analysis of how non-religious themes circulate in online spaces; digital ethnography and interviews with
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neuromorphic networks, we aim to design materials with learning capabilities. Our approach involves exploring multistability, hysteresis, and non-linearity in soft materials for memory, learning, and computation