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Field
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following areas: Probabilistic or Bayesian Machine Learning Variational Inference, Ensemble, or Diffusion Models Spatio-Temporal or Sequential Modelling Graph Neural Networks Deep Learning and Uncertainty
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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well as the underlying signaling network for a variety of diseases including aging, cardiovascular diseases, brain diseases, cancer, et al., through novel statistical or deep learning methods development and application
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3 years) in computer science, materials science, chemistry, physics, mathematics or related engineering disciplines Knowledge of deep learning techniques for time-series and image data Experience with
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Neural Networks Deep Learning and Uncertainty Quantification Python and ML frameworks (TensorFlow, PyTorch, JAX) Reproducible and open-science practices Experience with geospatial, environmental
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the time the employment decision is made. Research expertise in machine learning (e.g., deep learning, probabilistic methods). You have independence in research, curiosity, and strong critical thinking
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learning on graphs, including knowledge graphs, social and biological networks, financial networks, temporal and dynamic graphs, uncertain graphs, explainability and fairness of deep learning methods
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values of impact, safety, respect, integrity, and teamwork Preferred Qualifications Deep understanding of AI/ML concepts, including transformers, latent-space representations, generative models, and
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., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer
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to join our team at the CRSA to develop AI models, specifically deep learning approaches, to analyze and predict key climate variables such as precipitation and temperature which are essential