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Institute for Marine and Atmospheric Research is looking for a motivated PhD candidate with a background in physics, applied mathematics, meteorology, geosciences or a related field. You will work within the
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systems thinking mindset with robust mathematical frameworks to solve real world problems with our industrial collaborators at Rolls-Royce. Over the past 30 years, we have designed and introduced new
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complexity science, mathematical modelling in evolution, ecology and plant biology, along with transferable skills, including interdisciplinary communication and collaboration. You will also be offered
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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of the Earth system at different temporal and spatial scales to improve predictive capability. Comprehensive education: Enjoy numerous opportunities for scientific training, skills development and problem
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to regenerative agriculture. The aim is to make regenerative agriculture the new normal by 2040. We are looking for a PhD candidate who can develop mathematical models that help understand which soil
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computer science, data science, mathematics, or statistics. Practical or theoretical knowledge in automated synthesis/robotics. N.B. The candidates selected for interview will receive a preliminary assignment
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the difference between overseas and home level fees through other scholarships or self-funding. Duties and Responsibilities: The responsibilities include defining, with support from the supervisor, the research
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well as ecological interactions. These data will be analysed to understand similarities and differences between disturbed and undisturbed patches to assess resilience. If feasible, measures of resilience will be
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in thermodynamics, optimization, and control theory. Strong understanding of mathematical modeling, numerical optimization, and/or model predictive control (MPC). Experience working with large-scale