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Field
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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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-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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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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. Proficiency in Python and/or machine learning applications for data analysis. Ability to work independently and manage multiple research activities. Experience contributing to academic publications and
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in the annotated and non-annotated genome space. Writing optimised R- or python-based scripts and train group members on using these to analyse their own datasets optimally. Writing scientific papers
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virtual reality and/or augmented reality technologies. Programming skills (e.g., MATLAB, Python, R). Experience preparing outputs to peer-reviewed publication standards. Experience delivering presentations
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transfer imaging), Proficiency in neuroimaging analysis pipelines, including familiarity with tools such as FSL, MRtrix, ANTs, or equivalent, and programming or scripting in Python, R, or bash/zsh on Linux