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Field
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predictive machine-learning models from heterogeneous data. DSIP is actively collaborating with industrial partners and research organizations. DSIP is involved in developing Deep Learning solutions for time
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Spinoza's Theological-Political Treatise, this Schmidt Sciences-funded phase integrates large language models with computer vision to systematically uncover hidden networks of controversial printing during
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sorghum, using new whole genome CRISPR screening techniques that the lab has developed. The project is in close collaboration with computational approaches that model the genes and circuits that make cells
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Learning Sciences, Computer Science, Human-Computer Interaction, Informatics, Educational Measurement and Statistics, Educational Psychology, or a related field. Required Qualifications: Relevant experience
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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with causal inference methods or machine learning approaches Demonstrated experience in scientific writing and publication Ideal for candidates who: Have recently completed (or are near completion of) a
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: Prior experience running behavioral experiments is desirable, as is previous collaboration or engagement with researchers in economics. Familiarity with methods from machine learning will be a plus. All
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towards improving controllers in turbulent flows. On any given day, you may be called on to: Own and execute novel machine-learning workflows related to wind conditions and control performance. Post-process
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch
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your scientific and professional skills by: Designing and leading analyses that apply state-of-the-art generative machine learning models (e.g., VAEs, GANs, transformer-based models) to large-scale