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Field
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Zanna, the successful candidate will focus on developing generative machine learning models for complex dynamical systems for probabilistic forecasts. The postdoc will be expected to lead independent
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applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process industries; advanced process control (APC); model predictive control (MPC); digital
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Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and interpretable AI methods (SHAP, Integrated
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mathematical, statistical, and machine-learning-based analysis of complex data sets, such as hypothesis testing, supervised/unsupervised learning, linear models, etc. Experience with atlas-scale single-cell data
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: Education: Ph.D. in machine learning, computer science, engineering, physical science or related technical discipline. Experience: Expertise in developing and training AI models Proficiency in Python
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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) methods. Demonstrated proficiency in Python and machine learning frameworks (e.g., PyTorch, Jax, scikit-learn) applied to genomic/related datasets. Experience with sequence modeling architectures and
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of lipoic acid or its derivatives or other monomers for the synthesis of degradable polymers is highly desirable. Experience in machine learning models for the optimization of polymers. Demonstrated track
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Science, Computer Science, Data Science, Neuroscience, or a related field by the start date. Demonstrated expertise in computational modeling of human behavior or computer vision / machine learning