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learning and deep learning methods to analyze multi-omics data (genetic, epigenetic, transcriptomic, imaging, single-cell genomics and spatial omics data) with the goal of understanding the underlying
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Qualifications: PhD in experimental particle physics at the time of appointment. Preferred: Deep understanding of the particle detectors, particle identification, data analysis Machine learning experience is a
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, computer science, information science, data science, (bio)-statistics, (applied) mathematics, physics, or a related STEM field. Prior working experience with EHR data, machine learning, deep learning
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying
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ability to analyze large datasets Knowledge of coastal and nearshore processes Preferred Qualifications: Proficiency in statistical modeling and time series analysis Experience with machine learning or deep
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integrated circuits (IC) and printed circuit boards (PCB). Additionally, the candidate should demonstrate expertise in applying computer vision, image analysis techniques, machine learning, deep learning to IC