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statistical analyses using Stata and Python. Review relevant academic literature and summarize findings. Prepare tables, figures, and documentation for research papers and presentations. Support other research
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to financial and economic time series, working with programs such as MATLAB, Python, and C++. The chosen candidate will have the opportunity to take courses and attend seminars at MIT. The ideal candidate will
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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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transport modelling, and data science. Solid programming skills (ideally Python) and experience with data analysis. Self-motivation and curiosity to learn and grow independently. Strong communication skills
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modelling of the atmosphere using python and FORTRAN, optimising momentum and flux balances using AI tools. This training will equip them for a career in atmospheric science.
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: Comparative transcriptomics, orthology inference, positive selection detection, protein domain analysis, phylogenetic comparative methods Computational skills: UNIX/Linux, HPC computing, R, Python You will gain
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programming skills in Python/MATLAB, and an interest in digital twin technologies, cybersecurity and machine learning. Entry Requirements Acceptable first degree: Computer Science or related disciplines
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cell and spectroscopic analysers. Programming (e.g., R, Python) and machine learning for advanced atmospheric time-series analyses. Skills for presenting research at conferences and writing peer-reviewed
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mathematics Proficient in programming (Python and C++) Fluent in written and spoken English; knowledge of German is an asset Strong analytical and independent working style Excellent teamwork and communication
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electrical engineering, computational engineering, or a related discipline A strong foundation in power system modelling and simulation Solid programming skills (Python, C++, or comparable languages) Interest