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degree (2.1 +/MSc) in mechanical / civil / environmental engineering, physics or a related quantitative field; strong computing skills (Python/MATLAB/GIS); interest in transport or urban futures. Position
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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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. 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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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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research data. Strong analytical skills and proficiency in data management and programming tools such as SQL, REDCap, Python or R. Excellent organisational and problem-solving skills with an eye for detail