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this ambition. The ideal candidate will have a strong background in energy modelling, industrial energy use, or a related field, along with proficiency in relevant computational tools (e.g., Python
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of computer science, mathematics, applied mathematics or statistics. Applicants must demonstrate proficiency in machine learning or statistical modelling and have some experience with computing through Python, R or C
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: Applied Statistical Analysis (taught using R, two module sequence) Research Design for the Social Sciences Computer Programming for Social Sciences (taught using R and Python) As well as at least one of the
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and machine learning tools (e.g., Python, R, TensorFlow, or equivalent). Understanding of data protection and regulatory frameworks governing healthcare data. Strong communication and stakeholder
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model code (e.g. python) and analysing and visualising multi-dimensional data (e.g. Cran R). Knowledge of ML based analysis techniques, while not a requirement, would be beneficial to the role
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management and teamwork skills. 3. Strong data analysis and visualisation skills using suitable software (R, Python etc.). 4. Effective oral and written communication skills. DESIRABLE (but not
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. The candidate should have several years of demonstrated programming experience, preferably post PhD, in relevant programming languages such as Python, scripting languages (Javascript, Typescript), and
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Computer Vision, and Predictive Analytics. *Experience fine-tuning models using frameworks such as Keras, TensorFlow, Pytorch. *Strong software development skills using Python and other languages. *Strong
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focus on drug design computational chemistry. You have experience in one or several of the following areas: artificial intelligence, CADD, Python programming, or small molecule drug discovery. Applicants
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in embedded ML, is desired. Willingness to work on both bioelectronic circuits and embedded ML integration. Proficiency in computer programming languages such as Python and C++. Experience in peer