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Field
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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, i.e., machine learning models explicitly constrained by physical laws (e.g., conservation of mass, momentum, or energy) or designed to integrate physics-based models and data-driven learning
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, train, and validate advanced computational models and machine learning algorithms tailored to complex datasets. Collaborate with multidisciplinary teams including biologists, engineers, and clinicians
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Details Title Postdoctoral Fellow in On-Premise Computing for Autonomous Vehicles (Computer Architecture, Machine Learning and Runtime Systems) School Harvard John A. Paulson School of Engineering
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Prediction Systems (CHPS), or experience with one or more of the following hydrologic models, including Sacramento Soil Moisture Accounting model (Sac-SMA model), Variable Infiltration Capacity model (VIC
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Are you an atmospheric scientist looking to apply your expertise to real-world forecasting challenges in Africa? Machine-learning has the potential to revolutionise weather prediction in Africa, and
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techniques, or tight-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces. Working knowledge of machine learning
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Tool (SWAT model), and/or Noah Land Surface Model with Multi-Parameterization Options (Noah-MP model). Experience in either developing or applying Artificial Intelligence and Machine Learning tools and
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-binding approaches. Proficiency with major simulation packages such as ASE, Quantum ESPRESSO, VASP, CP2K, or LAMMPS, and their Python interfaces. Working knowledge of machine learning techniques in
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proceedings as defined in the project scope and agreements. Scientific activities include: 1) Develop Tidal Resource Models Design and implement statistical and machine learning tidal models to predict energy