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, government, industry and international research organisations, the School provides an exceptional environment for research, education and innovation with impact at national and global levels. To learn more
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. Utilize machine-learning and data-mining approaches to recommend bioengineering interventions. Develop new machine-learning algorithms. Integrate machine learning techniques with mechanistic modeling
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measurements and in the underlying physical models. Machine learning (ML) techniques can be exploited to identify common patterns in the data and augment the physical laws of wave propagation, leading in turn
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machine learning. The successful candidate will develop and apply methods that integrate multimodal molecular and clinical data (genomic, epigenomic, transcriptomic) across serial patient timepoints
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, quantitative characterization, and machine learning-enabled decision-making. The position is associated with the recently awarded ERC Consolidator Grant project “Engineering Multivalency for Superselective
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often and stress level is moderate to high. Noise level is quiet to moderate. Physical Activities Ability to work in front of a computer for extended periods of time. Occasionally required to move about
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graphs (ARGs). Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed
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We are seeking creative and energetic candidates with strong experience in multimodal machine learning and human behavior analysis and modeling for a one-year Postdoctoral position. Using recent
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, selectivity and phenotype mechanisms directly in primary human cells and employ machine learning methods for data analysis. This includes activity-based profiling, global- and phosphoproteomics and phenotype
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the Lotfollahi Lab – leaders in generative AI and foundation models for spatial and single-cell genomics – you will develop and apply state-of-the-art machine learning approaches to large-scale spatial genomics