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, Urban Studies, Urban Analytics, Environmental Science, Computer Science, Architecture, or an appropriate master’s degree. Familiarity with Python/R programming, GIS and spatial analysis (e.g., ArcGIS
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background in applied mathematics, computational mathematics, computer science, physics, or engineering is suitable. Basic programming experience (e.g., C, C++, Julia, MATLAB, Python, or similar) is necessary
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, Psychology, or a related field, to be awarded before March 1st, 2026. Essential skills include an ability to code (e.g., Python, R) and interpret data, knowledge of machine learning and statistics, and a
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. Candidates must have proven ability to work with large datasets, coding with Python/Fortran/C++ and ideally experience with high-performance computing. Applicants from an industry background are encouraged
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languages (Python/MATLAB) commonly used in machine learning applications, is desirable but learning can be completed during the PhD. Excellent communication and interpersonal skills to facilitate
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candidate with a strong quantitative background (e.g., in computer science, statistics, bioinformatics). The following skills are essential for this project: Excellent programming skills in Python. Proven
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: At least an upper second-class degree (preferably MSc) in a Science or Technology discipline. Good working knowledge of machine learning and deep learning. Hands-on knowledge of Python or PyTorch
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. The following skills are highly desirable but not essential: Ability to program in Matlab/Python Experience with Finite Element Analysis and Reduce Order Modelling Experience in Rapid Prototyping and CAD Design
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on the topic (2,4). Training and Development Training will maximise future employability in academia and industry: Programming and geospatial data analysis using Python/R. Machine/deep learning techniques
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. python, bash, matlab) Good administrative skills, and the ability to organise and prioritise workload Excellent interpersonal, oral and written communication skills Ability to work independently and as