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cellular processes efficiently. This project aims at understanding the formation and functioning of aggregate-forming Archaea-Bacteria partnerships. The project involves working with syntrophic deep-sea
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cutting-edge technologies — from single-cell multi-omics and deep learning to live-cell imaging and stem-cell–based organoid systems — to predict, observe, and manipulate epigenetic processes. Our lab and
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(or near completion) in computer science, machine learning, statistics/biostatistics, computational biology, data science, physics, or a related field. Experience with modern deep learningframeworks (e.g
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/ Deep Learning (particularly Computer Vision or 3D perception) Verification & Validation (V&V) of advanced algorithms, software or systems Formal Methods Safety Engineering / Safety-Critical Systems
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into global environmental changes, and ecosystem sustainability Experience with deep learning, radiative transfer modeling Teaching and supervision experience Who we are At the Department of Agroecology, our
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to explore new analysis methods using deep learning. Depending on the interest, it is also possible to be involved in the wet lab. Professor Tiwari also leads the Center for Integrated Multiomics in Precision
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data preprocessing, RNA splicing, deep learning and analysis of human multi-omics data is an advantage. The candidate should exhibit a high degree of independence but on the other hand also value a
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work is Hangøvej 2, Aarhus, 8200 and the area of employment is Aarhus University with related departments. The position will also require attendance at the Deep Tech Experimental Hub, in Aarhus North and
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to dramatically improve patients' lives and change the world? Are you excited to push the envelope of understanding with respect to the structure and kinetics of genome-editing? Do you love learning and developing
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to contribute to: (1) The design and build-up of a deep-UV supercontinuum source for ultrafast pump-probe transmission / reflection spectroscopy. (2) Investigate the dynamics of electrons and holes in selected