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modeling, interpretable and explainable machine learning, or hybrid modeling by combining process-based and data-driven approaches. Besides your own main project focus, you will contribute to the supervision
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of ceramic materials, polymers, as well as the associated sintering and joining processes. We are looking for a machine learning expert combined with a chemistry or materials science background
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Zurich and is supervised by Prof. Livia Schubiger. The candidate will work with the IRDS group on projects that leverage NLP, causal inference, and machine learning to explore norms related to gender-based
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magnetic moments of circularly polarized / chiral phonons in quantum paraelectric materials. Job description The postdoctoral researcher will develop machine-learned force fields trained on density
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image processing techniques and modern machine learning approaches to extract meaningful quantitative information from complex biological images. The successful candidate will contribute to projects
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of academic output. Presenting the project internally and externally, and preparing material for such presentations. Profile Mandatory Requirements: You have: Demonstrable experience with machine learning tools
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Location: Sofia, Sofia 1784, Switzerland [map ] Subject Areas: Quantum Computing / Quantum Computing Artificial Intelligence Computer Science / All areas Natural Language Processing Machine Learning
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please apply. Your research will be highly collaborative; you should be eager to learn new science and technologies as well as contribute to the group through knowledge transfer of your expertise. Job
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treatment of the xenon and argon liquid phase using electronic structure calculations. Developing computer code for computing ionization rates with general dark matter-electron interactions. Exploring
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Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a multidisciplinary team to accelerate functional thin film development Your work will be part of a larger