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. The research group led by Martin Enge is specialized in methodology-driven analysis of patient data, especially in the field of single-cell multiomics. We are a multidisciplinary group with expertise in both dry
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and career advancement across the globe. DDLS industrial PhD position We are announcing the position of Data-driven life science (DDLS) PhD student in data driven cell and molecular biology. This is an
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equivalent to one full year of studies Contribute to teaching at the division of Environmental Systems Analysis The following requirements are mandatory: To qualify as a PhD student, you must have a Master's
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aluminium through AI-driven microstructural analysis. About us The PhD candidate will work at the Division of Data Science and AI , in the neuro-symbolic research group. This group works with combinations
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aluminium in high-value products produced by mega-casting. The main objective of the PhD project is to develop a finite element analysis (FEA) framework that can accurately predict the mechanical properties
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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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qualifications Marine biogeochemical processes Hydrodynamic processes related to ships, turbulence, or mixing Oceanographic modelling Data analysis and programming (e.g., MATLAB, Python, or R) Interdisciplinary
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consist of the following: Mathematical analysis of ecological and eco-evolutionary models, involving pencil-and-paper calculations; Computer simulations of more complex models which do not easily lend
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information about us, please visit: the Department of Biochemistry and Biophysics . About the DDLS PhD student program Data-driven life science (DDLS) uses data, computational methods and artificial
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-scale GAC filter data to aid in system understanding, design, and optimisation. Main responsibilities Conduct research within the PhD project (>80%) Publish scientific articles and present your work