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. Responsibilities and qualifications You will contribute to the development of a computational framework designed to predict the degradation mechanisms of organic electrolytes. The framework will rely
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verify hits. Ideally, a fully automated and fast "loop" could be realized in which, 1, a promising material is predicted, 2, it is synthesized, 3, it is tested for activity and stability and, 4
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small-scale processing sector. By joining this project, you will contribute to the development of AI-powered tools that predict non-compliance, improve food safety monitoring, and ultimately protect
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and operation of building HVAC systems, these technologies support both energy efficiency and flexible demand objectives. Model predictive control (MPC), which involves physics-based building energy
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that can then be tested quickly in the lab rather than remain computational predictions? Do you also wish to work closely with experimental biologists and gain a solid grasp of how experimental work is
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domains. The scientific outcomes are expected to be significant in: Earth system science – by improving models of Earth surface evolution and enabling better predictions of landscape response to climate
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AI models. Identifying relevant modalities to enhance prediction performance, with a focus on multi-spectral sensors, will be a key research area. Additionally, anomaly detection for modalities other
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, or predictive modeling—based on real experimental data. You will work closely with engineers, technicians, and the postdoc to build and refine data pipelines and interfaces. As part of your research training, you
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generation by developing ML-based dual stabilization techniques. These techniques aim to predict and control the behavior of dual variables, reducing oscillations and improving the efficiency of the iterative
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) therapy on the biology of γδ T cells and how can we use this knowledge to help us predict the success of therapy and prevent the development of side-effects. Position 1 will focus on the cellular and