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mathematics. Strong programming skills in Python. Ideally, also proficiency in at least one major deep learning framework (e.g., PyTorch, JAX). An early track record of research (e.g., a high-quality Master's
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research. The ideal candidate will have a strong foundation in Python programming and hands-on experience with deep learning frameworks such as TensorFlow or PyTorch. Applicants with a background in Natural
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data from textual sources Conduct data analysis using econometric and statistical tools (STATA, R, or Python). Assist in literature reviews and summarising academic research. Contribute to writing
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structural dynamics, hydrodynamics, or marine systems Experience with programming or numerical tools (MATLAB, Python) A willingness to work across disciplinary boundaries Motivation to tackle open-ended, real
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, or Python. The studentship covers fees at Home rate (UK and EU applicants with pre-settled/settled status and meet the residency criteria). International applicants must cover the difference between Home and
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development (experience in multiple fields are considered beneficial). Familiarity with simulation software and numerical methods and proficiency in programming languages (Rust, Python, MATLAB, C/C
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finite element analysis; MATLAB or Python programming; An interest in electrified propulsion and industrially relevant research. Experience in electromagnetic design, thermal analysis, optimisation
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PhD Studentship: Advancing solid-state battery electrolyte manufacturing with terahertz spectroscopy
MATLAB/Python and signals processing Understanding of electromagnetics Genuine interest in battery technologies Experience with CAD and mechanical design How to apply: Candidates should submit
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, Machine Learning, or Smart Energy Publication record in peer-reviewed journals or conferences, commensurate with stage of career Good programming skills in Python, R, Java, or Matlab Experience
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analysis, such as MATLAB/Simulink, Python, finite-element tools or equivalent Essential Application/Interview Awareness/knowledge of field-oriented control, FPGA-based control, GaN inverter technologies