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Field
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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to groom the next generation of leaders, thinkers, and innovators to thrive in the digital age. Located in the heart of Asia, NTU’s College of Computing and Data Science is an exciting place to learn and
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analyzing hyperspectral data and developing machine learning models - Genetic or molecular lab experience - Bioinformatics, or statistical genetics experience - Excellent written and oral communication skills
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Osmosis (NF/RO) Membrane for Industrial Separation and Purification Application” Key Responsibilities: Integrating Machine Learning (ML) with Molecular Dynamics (MD) to predict phase-separation dynamics and
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for translatome analysis Expertise in integrating large-scale multiomic datasets, including machine learning based approaches Excellent written communication skills demonstrated by an outstanding publication track
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to regulatory agencies, investors, pharma companies etc. Learn more about the innovative work led by Dr. Don Ingber here: https://wyss.harvard.edu/technology/human-organs-on-chips/ What you’ll do: Develop models
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Jia Liu is seeking a highly-motivated postdoctoral researcher with a strong background in agentic artificial intelligence and machine learning. The successful candidate will conduct independent, high
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. This fellowship requires in-person participation in Manhattan, Kansas. Learning objectives: During this appointment, you will have the opportunity to: Gain experience in sorting and identifying insects of medical
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for relevant systems of EF-hand proteins · Supervise and instruct graduate and undergraduate students in Dr. Pengfei Li’s lab Minimum Education and/or Work Experience A MD or PhD is required in a closely related
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programming languages. Experience with DICOM data, medical-image registration, high-performance computing, or GPU-based computation. Familiarity with machine-learning or deep-learning methods for medical-image