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Field
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and synthesis of novel materials. Key Responsibilities: Develop and apply generative AI models for materials discovery, leveraging deep learning, Bayesian optimization, and active learning. Integrate
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& Development - Conduct original research in deep learning, related to uncertainty quantification for large models. - Explore cutting-edge advancements in AI and relate it to the main research objective
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-disciplinary AI research and learning. Incorporates advanced methodologies such as deep neural networks and language/multimodal models, leading the preparation of scientific papers and technical reports
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and/or presentation; may assist in grant writing. Stay informed about developments in the fields of stroke, medical imaging, and deep learning to maintain and enhance your professional expertise. Lab
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discipline. Experience and deep understanding of X-ray scattering experiments Experience with development of analysis code for experimental data in Python or similar language Strong interest in learning new
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looking for your next challenge? Do you have a background in machine learning or fluid dynamics and an interest in applying your skills to understand the dynamics of Earth’s fluid core and space-weather
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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deep learning. Strong analytical skills, a track record of publication in high-impact journals, and an ability to work collaboratively within a multidisciplinary team is essential. Key Responsibilities
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medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and astrostatistics. These posts
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-cultural and interdisciplinary team Adaptable and willing to enter into deep learning in new areas Fuent in written and spoken English with good communication skills Hiring Institution: LKC