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with expertise in geochemistry or mineralogy or materials science or other, who has experience in X-ray diffraction analysis (for example, with Rietveld refinement) and geochemical speciation
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experimental research-based technology integration, modeling, control, and data-driven analysis and toolset development. Here is the chance to work in a pleasant team atmosphere, dive into research and at
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to gain experience in cross-sector research collaborations and serving as a platform for a future research career in academia, hospital, or industry. Your role You will lead SIF project work package 2
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degree (PhD) in electronics, robotics, mechatronics, electrical engineering, embedded electronics, or a related subject area. The applicant is expected to have relevant research and teaching experiences
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create multi-fidelity predictive models that integrate data from quantum simulations and experiments, using techniques such as equivariant graph neural networks with tensor embeddings. We aim to train
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. Key responsibilities include conducting both experimental and theoretical research on the generation, manipulation, and utilization of three-dimensional cluster states for quantum computing
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solid understanding of energy system technologies and economics is also required. Additionally, experience with other programming languages, open-source software development, project management using
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the experiments. You will also have the opportunity to carry out your own simulations with our numerical model. Qualified applicants must have: A strong drive to move the frontiers of science. Ample experience with
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organization studies, sociology, or a related field. The project is based on in-depth qualitative research methods (ethnographic methods and discourse analysis). The position requires experience with
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run simulation experiments across a wide demographic and ecological parameter space Extract genomic metrics from simulated data Contribute to training and evaluating machine learning models