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Field
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where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a
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for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow
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where our people are empowered to thrive through supportive leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a
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/ESA_selects_Harmony_as_tenth_Earth_Explorer_mission ) The candidate will develop and apply cutting-edge remote sensing or photogrammetry methods, in combination with data science approaches such as machine learning and data assimilation via
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intelligence. Strong knowledge and research outputs in AI, particularly in the field of deep learning, knowledge representation and reasoning, or neuro-symbolic AI. Proven commitment to proactively keeping up
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experimental research involving imaging systems, instrumentation, data acquisition, and quantitative validation is highly desirable. Familiarity with AI/deep learning methods for medical imaging is advantageous
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setup, validate results using electrophysiological recordings, apply deep-learning approaches to analyse noisy data, and disseminate your findings through publications. The team – You will work with the
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· Lead the design and implementation of mixture-of-experts neural architectures and reinforcement learning pipelines for counterfactual disease trajectory simulation for EMED, an NIH-funded multi-modal AI
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leadership, shared responsibility, and a deep commitment to genuine care and respect for our community. Find out more about our vision for a truly inclusive workplace in our Diversity, Inclusion and Belonging
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opportunities within the company. Responsibilities Develop and implement advanced computational and machine learning strategies, including deep learning, graph-based methods, and probabilistic modeling