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workstreams, and the PhD’s will be working along senior staff to perform tasks in different workstreams, in strong collaboration with multiple international partners and fellow PhDs from all over the world. Key
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of topics include algorithmic fairness in network analysis, developing network embedding frameworks for real-world network datasets or AI models based on agentic LLMs for simulating real-world network data
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are seeking a highly motivated PhD candidate to develop efficient on-device generative AI systems based on large language models (LLMs). The project focuses on creating compact, low-latency, and energy
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across infrastructure networks, studying how infrastructure administrators handle uncertainty and complexity across multiple organisational levels when managing their infrastructure assets. The candidate
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paid parental leave and flexible terms of employment based on the CAO NU. In addition to the terms of employment external link laid down in the CAO NU, Utrecht University has a number of schemes and
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phenomena across scales, combining multiple fields including physics, mathematics, astronomy, history & philosophy of science, and social science. Its approach to societal engagement throughout the project’s
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for sustainability in real-life settings; conduct action research and stakeholder engagement; work collaboratively in a team across disciplines and with multiple stakeholders; participate in conferences, workshops and
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on addressing the complexity of the device-software-application-data design space, enabling systematic and efficient exploration using modeling and simulation tools.## Key Responsibilities- Identify and
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: Optimize network design: Develop a robust, scalable thermodynamic–hydraulic framework to size and configure prosumer-based heating and cooling networks under conditions of price volatility, CO2 taxation
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. Has experience with qualitative and/or quantitative research methods. Has excellent communicative and project management skills to handle the multiple stakeholders involved in the project (e.g