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to anticipate, detect and respond to emerging viral threats at an earlier stage than before. Coordinated by Prof. Dr. Heli Harvala, Professor of Virology at the University of Turku in Finland, the network brings
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Are you passionate about transforming cutting-edge science into compelling stories that inspire action and understanding? Do you enjoy communicating complex research in ways that engage scientists
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for compositionally complex, defect-containing and structurally disordered crystalline materials. Develop, train, validate and benchmark machine-learned interatomic force fields for multicomponent inorganic energy
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whose work may center on one or more of the following disciplines: Developing a mechanistic understanding of interactions of food macromolecules in complex matrices during processing or formulation
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organometallic chemistry and the synthesis of organometallic complexes. The candidate must be with the technologies for plastic recycling. The candidate must have good communication skills and experience in
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of the position is the development and application of finite element methods for modelling static strength and fatigue behaviour, as well as design and optimization of complex structures. You will work
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therefore expect you to have: Strong analytical and computational skills A passion for complex research and development projects Ability to work both independently and collaboratively in a dynamic research
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(PSAI), covering probabilistic, symbolic and neuro-symbolic approaches to AI, and Data Engineering, Science, and Systems (DESS), focusing on methods and systems for managing and analysing complex data
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of two years. About the Project The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems
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published in international peer reviewed journals. Understanding of the atmosphere and its modelling is essential. • Model development of WRF-Chem is complex. Documented experience using FORTRAN, C