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Communication skills, Machine learning, artificial intelligence, data science, Experience in geospatial science projects, Experience of Python 3.1x, Python machine learning libraries and Python geospatial
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SPEAR Centre: PhD in ‘Long-Range, High Bandwidth Distributed Acoustic Sensing for Fibre Optic Links’
fading, and maintain stable performance over 40–100 km fibre lengths. Perform co-simulation of optical and electronic/system-level subsystems (e.g., using Python, MATLAB, VPI). Build and characterise
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for integrating Python scripts, research on the decision and prediction models, and developing a user-friendly web interface. Salary Scale: €46,000 - €50,000 per annum Appointment on the above range will be
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engineering Machine learning or AI methods (e.g. anomaly detection, classification, regression, time-series modelling) Programming skills (e.g. Python, MATLAB or similar) Experience with industrial systems
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1. Working knowledge of mixed-method research techniques. 2. Experience conducting research with vulnerable populations. 3. Experience with data analysis using Python, R, or similar software. 4
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or equivalent Skills/Qualifications Communication, Organisational skills, Research skills, Crispr Cas9 to E. coli, Python, R, Java, C, C++, Statistical analysis, Experience in Biosensors, Experience in 3D
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metrics (OEE, MTTF, MTTR). Programming and analytics experience (e.g. Python, MATLAB). Experience engaging with industrial stakeholders and managing site-based research activities & strong organisational
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independent research to a high level. A working knowledge of R. and/or Python programming is desirable. The candidate will be based in the laboratory space in the School of Pharmacy, UCC, working with Professor
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experience in Python, R, Java, C, C++ or related language to process real-time experimental data Statistical analysis Demonstrated understanding of operational requirements for a successful research project
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or equivalent) Strong programming proficiency in Python Experience with deep learning frameworks such as PyTorch and/or TensorFlow Experience using data science libraries (e.g. NumPy, Pandas, SciPy, scikit-learn