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with remote sensing data (satellite, aerial, hyperspectral, SAR, LiDAR) Computer Vision Natural Language Processing Remote Sensing Machine Learening and Deep Learning Reinforcement Learning Large
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application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows
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. The successful candidate will investigate technical challenges and deployment risks associated with TES integration, and develop data-driven understanding of industrial energy use and storage
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research assistants, fostering a productive and supportive lab environment. Analyze, interpret, and integrate large-scale datasets (e.g., genomics, transcriptomics, proteomics) to generate actionable
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approaches to large-scale biomedical data. Research topics may include foundation models for longitudinal electronic health records and genomics, multimodal learning integrating health records with molecular
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: Design and implement ML, deep learning, and Large Language Models for orthopaedic applications Work with multimodal clinical data, including: Medical imaging (X-ray, CT, MRI) Electronic health records (HER
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QTLomics as part of the project. Main responsibilities Collect and standardise functional information, including QTL data, RNA-seq, and Gene Ontology (GO) annotations Develop computational pipelines for QTL
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in machine learning and/or computer vision as applied to robotics. - Strong publication record and demonstrated research independence. The referenced salary range is based on Johns Hopkins University's
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researcher in Computer Science, Data Science, Human-Computer Interaction, Psychology, Empirical Educational Research, Learning Sciences, Cognitive Science, or related disciplines who is eager to contribute
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large and/or heterogeneous datasets, including quantitative and/or qualitative data Experience working with project partners and interdisciplinary research teams Required Education PhD in environmental