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Field
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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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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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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and
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. Significant experience of developing deep learning methods using computational frameworks such as PyTorch, TensorFlow etc. Experience of working with molecular questions in the biosciences An interest and
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will work with tools such as AlphaFold and RosettaFold-diffusion and apply deep learning to address the next challenges in dynamics and the design of novel protein-based therapeutics. As postdoc, you
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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