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Field
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candidate with a background in marine science, biological oceanography, environmental science, data science, computer science or related discipline. Strong analytical, numerical and practical skills are
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scientists who are active in the development of mathematical models or numerical methods in partial differential equations, in connection with uncertainty or data science. Specific fields of interest include
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, Physics or a related field strong programming skills, e.g. in Python, Julia or C++, and experience in writing clean, well-tested code experience in numerical methods, optimisation, statistical inference
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assessment (LCA). You will formulate and implement analytical and/or numerical models, define objectives and constraints, and explore trade-offs through Pareto analyses and sensitivity studies. Using
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for interdisciplinary research at the interface of energy systems, high-performance computing, and quantum computing Strong background in mathematics, optimization, numerical methods, or scientific
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analytical and numerical methods to solve partial differential equations, -- excellent oral and written communication skills. Prior experience in computational fluid dynamics or active matter will be a big
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probability, statistics, optimization, numerical methods, or quantitative modeling * Experience developing research code in Python and working with large datasets; experience with C++, scientific
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background We are looking for candidates with a strong background in one or more of the following areas: Modelling and simulation of variable speed electric machines Numerical methods for finite element
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provides rich datasets with well resolved spatial and temporal measurements. The overall aim of this PhD project is to develop the next generation of advanced data analysis and characterisation methods for
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methods are reshaping the foundations of pure mathematics, while abstract mathematical concepts generate fresh insights into algorithms and discretization techniques – critical for numerical computations