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estimation). They have however been developed and tested using mostly simulated HydroGNSS data, or data from past GNSS-R missions, and as such they may be sub-optimal when applied to real HydroGNSS data
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will provide fundamental insights that help optimize chemical recycling processes, bringing Europe one step closer to a truly circular plastics economy. This PhD project is part of a national research
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for residential energy management systems and energy hubs, with particular emphasis on distributed optimization, cyber-security, and quantum computing for real-time decision-making. Ultimately, the goal is to equip
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each other. This necessitates a multidisciplinary approach bringing together optimization, machine learning and behavioral modeling methodologies. In FlexMobility we propose a holistic approach to design
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that there are plenty of challenges ahead for you. You will work here Making the world digitally accessible to others means achieving optimal digitally sustainable information management. Our department
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); Experience in mathematical modeling of cognition or behavior (e.g. optimality, drift-diffusion); Advanced computational and/or programming skills (Matlab, Python, C++, R, or other languages); A commitment to
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infections. As a post-doctoral researcher, you will work at the intersection of data science, epidemiology, and clinical research, developing innovative methods to optimize the use of rich real-world data. You
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(quantity is less important); Experience in mathematical modeling of cognition or behavior (e.g. optimality, drift-diffusion); Advanced computational and/or programming skills (Matlab, Python, C++, R
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learning, with a focus on novel optimization techniques and memory architectures. Advancing the reasoning capabilities of large language models (LLMs). Developing methods to improve the safety and robustness
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PhD Position on Machine Learning Detection of Positive Tipping Points in the Clean Energy Transition
of simulation, optimization, and policy modelling. You will also connect with TU Delft's strong internal network on climate action, particularly through the Climate Action Programme and its Climate Governance