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Field
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pavement networks. This position will focus on modeling and optimizing pavement performance under evolving climate and hazard conditions. This position will advance modeling of pavement life-cycle
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-Lab in Neuchâtel, within an international and multidisciplinary research environment with strong links to the photovoltaic industry. Main duties and responsibilities You will develop and optimize PV
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energy systems and datasets. Validate and benchmark the developed models using building measurements, physics-based simulations and energy-system optimization models. Investigate how tabular foundation
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developing learning-based perceptive approaches. You will develop learning and/or optimization based approaches to multi-robot path and motion planning and coordination, integrating multi-modal perception and
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and highly interdisciplinary research environment, bringing together expertise in theory, nanoparticle science, OLED technology, and organic chemistry. You will work closely with two ongoing PhD
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related fields). Holding a PhD degree, obtained upon completion of doctoral studies. Programming skills (Matlab, Python, etc.). Ability to work with large datasets. Knowledge of basic statistics. Ability
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with national quantum strategies, such as Canada's National Quantum Strategy. The recruited candidate will participate in a Franco-Canadian collaboration focused on the study and optimization
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for developing and optimizing plasma-based functionalization processes for ferrimagnetic nanoparticles. The successful candidate will be responsible for investigating the relationships between plasma parameters
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to develop, optimize and apply advanced mass spectrometry approaches for the structural and molecular characterization of ARDCs, and to analyze and interpret the resulting data. - Develop and optimize mass
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The project's main tasks focus on the development and optimization of new retroelement-based genome engineering tools; the design, execution, and analysis of molecular and cellular biology experiments; and the