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models. Experience in large-scale deep learning systems and/or large foundation model, and the ability to train models using GPU/TPU parallelization. Experience in multi-modality data analysis (e.g., image
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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candidate with: A PhD degree (or equivalent qualification) in AI or data management A strong scientific track record, documented by publications at high quality conferences and journals (e.g., SIGMOD, NeurIPS
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environment for all employees through mutual respect and tolerance. Description of the project The postdoc will leverage existing high-throughput data from large scale cohorts and large family cohorts
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of health outcomes in the general population using molecular, socioeconomic, and environmental data. You will work with very large, globally unique registry, medical, and molecular data from CBS and ELAN
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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about our research can be found here . Information about Alexander Holm can be found here . Qualifications By the start date, you have graduated with a PhD in mathematics, computer science, physics
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to large-scale energy storage systems. Despite their widespread use, these batteries are subject to complex and interconnected degradation processes that can significantly impact performance and lifetime
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. A major focus will be on the identification of small molecules from mass spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available
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Post-doctoral Researcher in Multimodal Foundation Models for Brain Cancer & Neuro-degenerative Disea
response to therapies, with applications in brain tumours and neuro-degenerative diseases. Our data comes from large public cohorts as well as local proprietary databases of clinical and preclinical medical