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contemporary data-driven techniques. Computational methods such as optimization, filtering algorithms, predictors, etc. Software and coding skills with, e.g., Python, MATLAB, R, C++, Julia, potentially HIL
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and thermophysical properties. Furthermore, the group excels in development of efficient phase equilibrium calculation algorithms for compositional simulations. The project is supported by a robust
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candidates in any field of machine learning theory, algorithms and computation or in AI applications in e.g., the social, neuro, or natural sciences, are encouraged to apply. The position comes with a start
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goals Teach and co-supervise BSc and MSc student projects Participate in Arctic field campaigns We expect you to have: Experience in working with large data sets and development of algorithms. At least
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”, i.e. being capable of performing with very high efficiency all year round thanks to their advanced control system, through: the demonstration of a dedicated control algorithm for ejector-equipped
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across the value chain. Using Bayesian Optimization / Modern Design of Experiment, we build the data-foundation to enable true hybrid development between humans and advanced learning algorithms such as
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methods such as optimization, filtering algorithms, predictors, etc. Software and coding skills with, e.g., Python, MATLAB, R, C++, Julia, potentially HIL. Excellent command of English in speech and writing
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programming Creating their own mechanical designs, implement and test them accordingly, Implementation of control algorithms on physical experiments. In addition, the candidates are expected to contribute with
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Examples of research areas that the Associate/Tenure Track Assistant Professor might cover: Digital transformations of organisation and management Studies of technology and data-driven, algorithmic
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Examples of research areas that the Associate/Tenure Track Assistant Professor might cover: Leadership for societal transformation and systems change Leadership and algorithms Business leadership and