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Field
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learning techniques and experience with coding in Python is beneficial (but not a strong requirement) Applicants whose mother tongue is not English must meet any one or a combination of the following: A
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practical experience in at least one programming language (preferably Python) Ideally, some practical experience in material characterization methods Structured and analytical thinking as well as a systematic
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, nanofabrication, and computational electromagnetism. Strong coding (Python /MATLAB) and experimental aptitude is desirable.
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Matlab, Python and C/C++ are preferred. Strong interpersonal skills and the capacity to work and learn independently will be required. English Language IELTS 6.0 Overall (with no individual component below
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, energy-related datasets. Proficiency in Python, MATLAB, and/or Julia for modeling, simulation, and data analysis. Familiarity with GIS tools (e.g. QGIS), time-series databases (e.g. InfluxDB), and version
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, computer science, or a related field with an overall grade of at least “gut” (or equivalent, e.g., cum laude) Expertise in quantum mechanics, experience with HPC, programming (e.g., Python, C / C++), and/or
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Retrieval-Augmented Generation (RAG) for data retrieval and knowledge inference implementation of your machine learning pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with
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programming experience (preferably in Python). If English is not your first language, provide evidence of proficiency through: A minimum IELTS average score of 6.5 and a minimum of 6.0 in each component. OR A
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Energy simulation software, such as TRNSYS, Modelica, EnergyPro Programming languages, such as Python Ability to communicate results in technical reports, and prepare scientific papers for publication in
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MATLAB/Python/R etc Previous research experience with healthcare datasets or electronic health records English Language IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University