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, or district energy systems. Experience with system optimisation and techno-economic analysis of energy systems. Proficiency in scientific computing environments such as Python, MATLAB, Julia, or Modelica
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writing skills, and have a track record of scholarly activities including publication of research findings in reputable peer-reviewed journals and conference presentations. Experience using R or Python and
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Python and C/C++. Expertise in ensemble learning (e.g., Random Forests, Gradient Boosting, bagging/stacking frameworks). Hands-on experience with parallel or GPU-based computing (CUDA, OpenCL
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and analysis (e.g., Python with pandas/numpy, R, Julia, or other suitable languages) and experience working with tabular datasets. Basic ML knowledge (baseline models, leakage-aware splits, ROC-AUC/PR
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colleagues to achieve team goals. Excellent proficiency in the following areas is preferred: 1. Microsoft Office Applications, particularly MS Word, Excel, Powerpoint 2. Experience in R, Python The successful
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, particularly MS Word, Excel, Powerpoint 2. Experience in R, Python The successful applicant is expected to possess at least a Bachelor’s Degree in Epidemiology, Public Health, Environmental Health, Biological
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. Proficiency in algorithm development using Python will be advantageous Where to apply Website https://www.timeshighereducation.com/unijobs/listing/407953/research-fellow-ai-… Requirements Additional Information
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example of previous publications where the candidate performed such work. ● Generate user friendly scripts in R, and Python for data visualization and analysis that can be used by other team members
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related field. Experience The ideal candidate should have demonstrated experience in several of the following areas: Extensive experience with python and modern C++ in a Linux/UNIX environment, including
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such as R, Python, or Julia. Excellent communication and collaborative skills. Strong publication records in peer-reviewed journals is an advantage. Experience with biological data (e.g., genomics