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intelligence, starting 1 April 2027. You will design new sequence-to-expression deep learning models that predict gene expression from DNA sequence in plants, using new experimental datasets generated in the
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, Denmark, invites applications for a two-year postdoctoral position in plant regulatory genomics and artificial intelligence, starting 1 April 2027. You will design new sequence-to-expression deep learning
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relevant variables such as peptide sequence, precursor charge state, collision energy, and peptide modifications into computational frameworks Evaluate model performance using suitable similarity
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Build data-driven models for peptide fragmentation analysis using experimentally generated spectra Integrate relevant variables such as peptide sequence, precursor charge state, collision energy, and
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and MAS solid state NMR, HR-MAS, pulse sequence development, low-field NMR, high-pressure NMR and imaging, and a strong focus on modelling of solid-state NMR experiments. The lab is equipped with state
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generative models Is highly proficient in Python, PyTorch and/or JAX Has experience training large-scale neural networks on HPC or GPU clusters Has experience with representation learning and sequence or
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multiple object tracking (MOT) paradigm has identified many fundamental aspects of this process. With a rich literature of empirical results, this paradigm is ready for theoretical development, which
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. Secure collaboration facilities in the DTaaS platform allows co-creation of products and services across multiple companies and authorities, thus supporting large virtual enterprises necessary for the
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, decision-makers, and the wider public? Are you an exceptional writer with a talent for storytelling, scientific editing, and creative communication across multiple platforms? Then join us in telling
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one of the most pressing scientific challenges of our time: understanding how biodiversity is changing in response to multiple, interacting global change drivers. The project will combine cutting-edge