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, including parsing and processing large document corpora. Strong understanding of machine learning or AI methods applied to health or biomedical data. Demonstrated ability to assess model outputs, identify
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the shallow subsurface (<10 meters depth). Experience with soil moisture/salinity and sapflow sensors. Experience using neural networks and machine learning tools. Stipend $70,000.00 – $80,000.00 Yearly Point
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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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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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, 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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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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) will be required for this position Certificates/Credentials/Licenses PhD required in related field. Computer Skills Academic and research software relevant to job duties (GSAS II, OriginLab, https
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Schemes of Service: Research Division: Infocomm Technology Employment Type: Fixed Term As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research
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. - Criterion 2: Knowledge in the scientific areas of the project: Academic or applied knowledge in Software Engineering, Intelligent Systems/Machine Learning, and Interactive Technologies. - Criterion 3
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, mathematical modeling, optimization, and scientific computing. Experience in computational imaging, inverse problem solving, machine learning, or artificial intelligence is highly desirable. Excellent