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concept is the use of plasma technology. Your tasks This PhD position will contribute to the SNSF-funded Ambizione project Plasma4Water . Your main task will be the design, fabrication and optimization
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Swiss Federal Institute for Forest, Snow and Landscape Research WSL | Switzerland | about 23 hours ago
The Swiss Federal Institute for Forest, Snow and Landscape Research WSL is part of the ETH Domain. Approximately 600 people work on the sustainable use and protection of the environment and on the handling
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for their long-term performance. To fabricate and make these materials durable, the core concept is the use of plasma technology. PhD position in catalytic plasma coatings for water treatment Your tasks This PhD
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are you applying for this position? 2. What do you like of the topics of the research project? 3. Which of your past experiences best prepares you for this research? 4. What do you hope to achieve during
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physicochemical properties, especially susceptibility towards alcohol disinfection, stability upon desiccation and the ability to be transferred from one surface to another by touch. The use of DNA as a reporter
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simulation. The group also performs research on optoelectronics that are based on nanoparticles, offering unique opportunities to collaborate and to investigate the use of nanoparticles in Li-Ion batteries
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dynamic research group in a state-of-the-art-equipped environment Dedicated supervision and career mentoring A fully funded PhD position Application / Contact Please use the link below to apply and submit
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Bachelor's degree thesis Transcript of records of Master's and Bachelor's degrees. 2. Motivation statement in which you shortly address the following questions: Why are you applying for this position? What do
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for interdisciplinary collaboration and international networking A world-class training environment within the ETH Domain. Employment Conditions Start date: Flexible. Contract duration: 1-year renewable, up to 4 years
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the use of hierarchical graph neural networks for modeling multi-scale urban energy systems. By combining advances in Physics-Informed Machine Learning (PIML) and Graph Neural Networks (GNNs) with real