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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
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conclude on December 31st 2029. The goal of this research effort is to apply machine learning (ML) techniques, in particular (equivariant) graph neural networks to accelerate the creation of all physical
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Medicine (https://www.irem.uzh.ch/en.html) combines cutting-edge stem cell and bioengineering research to advance regenerative therapies. Based on bio-inspired principles and novel biomimetic technologies