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with data analysis/modelling and programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication
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magnetoencephalography (MEG) and behavioral tests Data analysis using Matlab or Python (speech-brain interactions, synchronicity measurements, connectivity measurements between sources) Presentation and publication
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experiment on a metric-scale concrete wall, using the multi-physics NDE probe integrated onto drone platforms. Expected Results A Python-based multi-physics Maching Learning framework for NDE characterization
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Thermodynamics • Ability to program in Matlab or Python The following skills are desirable but not essential: • Experience in numerical methods (e.g. finite elements) Application Procedure Informal enquiries
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skills : We expect a candidate with a strong background in machine learning or statistics. The candidate must also be proficient in high-level languages like Python. Familiarity with single-cell date and
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, particularly MRI, medical physics or computational data analysis (Python/R/MATLAB, machine learning, or bioinformatics) is highly desirable. Interested candidates should send a CV to michael.chappell
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. • be located at the agreed project location(s) and, if required, comply with the university’s external enrolment procedures. Selection criteria Skillset: Proficient in Python, machine learning, and
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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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of our experienced group members. • Participate in the development of theories, collaborate with the top experimentalists, and perform numerical simulations (matlab/python/mathematica). Key Requirements
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level or higher). Hands-on experience with Python and AI frameworks/tools. Nice-to-haves (Bonus Skills): Knowledge of vRAN, O-RAN architectures, and the Edge/Cloud continuum. Experience in LLM fine-tuning