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Field
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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methodologies to analyse multimodal data, enabling early detection and personalised interventions in clinical neuroscience. The candidate will take the lead on machine learning and computational analyses
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design and implementation of visual cues detection on video datasets (face and gesture), context-aware multimodal analysis, machine learning/self-supervised learning for automatically detecting subtle
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of childhood allergic diseases. The candidate will develop machine learning–based biomarker prediction models to identify microbiome-derived signatures associated with allergy risk and immune tolerance outcomes
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Loh on conducting research at the interface of Machine Learning and Microscopy under a project on Learning Spatiotemporal Motifs In Complex Materials. The main responsibilities of the position include
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an air force base involving UAV activities Visit to a navy base Battlefield walk ("staff ride" learning experience) Visit to the security fence and a checkpoint to learn operations/procedures Visit
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digitalization and computation. To further develop machine learning tasks for scent signal classification/fusion. Set up and analyze experiments under different conditions. To propose a methodology/framework in a
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including functional enrichment (GO, KEGG), network analysis, genome assembly and binning, systems biology, and multi-omics integration. Apply statistical modelling, machine learning, and deep learning
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Responsibilities: Electrochemical process on interface phenomena Battery testing under different conditions Simulation of scaled up process. Interface with machine learning group on data base set up Battery safety