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reclamation pilot-scale and lab-scale systems. Conduct membrane and separation process modelling, module-scale desalination system modelling, including conventional modelling and machine learning-based
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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, or behavioral data) and be proficient in Python and modern deep-learning frameworks (ideally PyTorch). Experience in computer vision, multimodal data fusion, self-supervised or generative modeling is highly
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in the 2025 QS World University Rankings by Subjects. We are hiring a Research Fellow in Signal Processing and Machine Learning to develop signal processing and machine learning algorithms and methods
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clinical approaches, including: Histopathology and digital pathology (whole-slide imaging, WSI) Quantitative analysis of the tumour immune microenvironment AI-based image analysis, machine learning and deep
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, proteomics, metabolomics), Capacity to develop and/or apply : Statistical or mathematical models Machine learning / AI methods Systems biology modeling approaches Research position The fellow will conduct
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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context. • Conduct statistical analyses, longitudinal modelling, or machine learning approaches as appropriate. • Develop documentation, codebooks, or tools to support reproducible research. • Lead
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. Integrate physical laws, experimental data, and simulation results into unified machine learning frameworks to improve model robustness and generalizability. Conduct data preprocessing, model training, and
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programming such as Python, R, MATLAB, or other similar programs and experience in using simulation/optimisation models and advanced data handling techniques e.g. machine-learning techniques, statistics