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highly skilled Postdoctoral Fellow with a proven dual‑mode research profile capable of independently performing laboratory experiments and coding predictive AI models in Python to forecast biomaterial
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physics, with experience in thin-film deposition, device fabrication, and electrical transport measurements Familiarity with the theory of magnetism Proficiency in MATLAB/Python and LabVIEW for data
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aquatic biogeochemistry and watershed-scale monitoring Fieldwork proficiency including sensor deployment Analytical and modelling skills with R or Python Excellent scientific communication Proactive
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governance. Proficiency in SPSS, Python, R, or MATLAB for data analysis and visualisation. Experience in designing and evaluating exercise/prehabilitation programmes. Track record of publications and ability
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applications in healthcare Proficiency in CAD software and 3D printing technologies Strong programming skills (e.g., Python, C++, MATLAB) Excellent written and verbal communication skills Ability to work
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learning and/or deep learning methods to biological or clinical datasets. Proficiency in Python and R, with strong experience in Linux/HPC environments and workflow automation. Track record of publications
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at least one scientific computing environment (e.g. Python, MATLAB, Modelica); experience with geothermal or reservoir modelling tools is an advantage. Research & Delivery Skills Ability to independently
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health, behavioural, or social science datasets. Proficiency in Python and/or R, and familiarity with open access AI/ML libraries. Track record (or strong potential) of peer-reviewed publications
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interventions. Candidates need to be able to understand statistical modelling, have a mathematical background, and be fluent in R programming. We will also consider candidates who have extensive C++ or Python
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(e.g., Python, Julia, Fortran, C++, MATLAB) and experience with high-performance computing environments Demonstrated publication record in reputable peer-reviewed journals Excellent written and oral