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challenges, and we are currently moving the code to a new python based High Performance Computing enabled modelling framework. This is an exciting opportunity to contribute to a high-impact scientific codebase
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skills, preferably Python Knowledge of machine learning Willingness to work on a cross-disciplinary project. *for students with an education earned outside of Sweden, a 4-year Bachelor’s degree is accepted
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learning, and control is essential. Applicants should also demonstrate: High academic achievement in relevant undergraduate and graduate courses Proficiency in programming (C/C++, Python) and experience with
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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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languages such as Python or C Solid background in quantum mechanics Experience from working on quantum error-correction, open quantum systems, quantum optics, bosonic codes or continuous-variable quantum
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include: Developing the model using open-source Python software Planning and conducting experiments Analysing teardown reports and experimental data Validating and improving the model Publishing results in
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with applications in the infectious diseases and the transmission of antibiotic resistance genes. Strong programming skills in Python or equivalent programming languages is a requirement. Since the
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should be attached): Previous projects or course work related to machine learning implementations Previous projects or course work involving mathematical proving Solid knowledge of Python What you will do
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accepted. Strong written and verbal communication skills in English Solid knowledge of Python and libraries like PyTorch and RDKit The following experience will strengthen your application: Knowledge in deep