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techniques (geomorphic mapping, TruPulse, DGPS/drone surveys), engineering geology methodologies (slope stability, rock strength assessment), coding (python/matlab data analysis and modelling) and transferable
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self-harm rates. You will expand your data wrangling, analytical and programming skills on python and develop expertise in the fields of machine learning, epidemiology, big data analysis, and suicide and
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programming (e.g., Python, MATLAB). Energy system modelling expertise with experience in academic research Preferred Skills: Educational background in Electrical Engineering, Computer Science, Renewable Energy
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Engineering or Computer Science. We would also like to see experience in: Machine Learning, Optimisation, Python, finite elements How to apply: Stage 1: Submit your 2-page curriculum vitae (CV), transcripts and
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equivalent in a related discipline. This project would suit someone with: Experience with programming (Python, MATLAB), background in aerospace, computer science, robotics, or electrical engineering graduates
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written and oral communication skills Knowledge, Skills and Experience Strong machine learning experience and proficiency in the state of the art data science languages (e.g., python, R) Experience in
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environment Proven ability to use a scientific programming language such as python or MATLAB for signal processing A desire to improve therapies available to patients with neurological conditions Excellent
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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predictive checking, model comparison) • Computational modelling with Python and Dynesty, JAX, NumPyro, and PyTorch • Use of asteroseismic and spectroscopic survey data (e.g. PLATO, Gaia, APOGEE, TESS) • High
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PhD Studentship: LLM-Based Agentic AI: Foundations, Systems & Applications – PhD (University Funded)
. Proficiency in programming and modern ML tooling (e.g., Python, PyTorch); experience with LLMs (e.g., using Hugging Face libraries) is a plus. Ability to reason about complex systems and turn ideas into code