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Field
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experimental chemistry, providing a supportive research environment. Applicants should have a PhD in Chemistry or related field, and extensive experience in python programming and machine learning models
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participation in conferences and events. Desirable It is desirable that the candidate has a Masters or PhD in electronic engineering, computer science or equivalent Fluent in Python and Python Libraries: Scikit
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field. Experience in the following is essential: single-cell fluorescence microscopy, microfluidics, image analysis, and machine learning (as applied to biological imaging). Python and MATLAB programming
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: single-cell fluorescence microscopy, microfluidics, image analysis, and machine learning (as applied to biological imaging). Python and MATLAB programming, especially in image analysis, are also essential
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-level optimisation using TRNSYS and MATLAB/Python. The associate will use advanced characterisation (e.g., DVS, porosimeter, DSC/LFA) to develop and assess adsorption composites, and apply CFD to analyse
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the discipline and of research methods and techniques to work within established research programmes. Applicants will have coding skills in Python, PIP, JSON and HTML and will be experienced in using MongoDB
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Python, using GPUs), with the precise balance determined by the candidate’s background and interests. The mathematical tools involved will include matrix analysis, optimization, backward error analysis
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(NFQ Level 8) or Master’s (Level 9) in a relevant discipline plus 3–4 years applied professional experience in AI/ML development. Strong programming and applied AI/ML development skills (Python
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skills (Python, TensorFlow, PyTorch, scikit-learn, etc.). Excellent stakeholder engagement and communication skills, with a record of working with SMEs or innovation-focused organisations. Desirable
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, MRtrix, ANTs, or equivalent, and programming or scripting in Python, R, or bash/zsh on Linux/HPC environments. Experience working with clinical research data, including neuroimaging and clinical data