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magnetic multilayers and nanostructures can be measured, understood, and engineered for spintronic and related applications at the nanoscale. Your profile Essential PhD in physics, nanoscience, or a related
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to high-impact publications. About you You hold a PhD in Biomedical Sciences or related fields. You have a strong interest in epithelial biology, stem cells, and disease modelling. You have experience with
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who thrives in a collaborative and interdisciplinary research environment. The ideal candidate possesses a PhD degree in chemistry or chemical engineering, materials science, physics, or related
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. Required PhD in Computer Science / AI / Machine Learning Strong publication record in AI, ML systems, or related areas Strong programming skills in Python, C/C++ and experience with PyTorch, TensorFlow, JAX
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You will have access to teaching assistants, research and travel funding, an international network of collaborators, and the wide variety of ETH career development resources The working language is
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methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs) Contribute to the strategic direction of research
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pipelines to support the labs long-term research interests Proactively manage the labs genomic data resources Supervise and mentor PhD and Masters students in comparative fungal genomics Assist in
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(RuzicaDadic), University of Fribourg (Martina Barandun and Horst Machguth), and ETHZurich (Evan Miles). The core team consists of the 4 PIs, 4 PhD students, and 4 Postdocsand aims to quantify the impact of
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: Experience with analyzing GPS tracks Good data-handling skills and ability to use R (compulsary) and preferably also Python and/or GIS competently Statistical/causal inference knowledge PhD degree in a related
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extraction, alignment, QC metrics, drift/batch correction) and reporting. Advance annotation strategies using modern approaches such as spectral/structure fingerprinting, molecular networking, in-silico