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. Applicant should have experience in time-series processing with appropriate AI models (recurrent networks, LSTM) and experience in 2D convolutional neural networks in Python. This is a part-time position (5
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2D convolutional neural networks in Python. This is a part-time position (5 hours/week) funded until 31/03/2026 with a possibility of extension and is suitable for a Ph.D. student with relevant
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interest and research in the field of economic and experience in data management and analysis. Demonstrable experience of working with quantitative data and relevant software (Stata, R, Python, or similar
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. This research is ideally suited to candidates with interests in photonics, metamaterials, ultrafast optics, nanofabrication, and computational electromagnetism. Strong coding (Python /MATLAB) and experimental
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will be required to demonstrate their ability to identify fundamental flow features and model these using suitable CFD methods. Experience in Fortran/C/C++/Python/Matlab is an advantage but not essential
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. Experience with EEG, fNIRS, or behavioural experiments. Programming and data-analysis skills (e.g., MATLAB, Python, R). Funding: This position is funded by the EU-funded HUM.AI.N-ACCENT Marie Skłodowska-Curie
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and communication skills, experience working with data in either R or Python, a medical degree, experience in functional genomic analyses, and experience working with people with lived experience in a
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computing paradigms (e.g., MPI, CUDA, etc.); designing and managing new electromagnetic solvers and associated codebases in C++ and/or Python. This is a particularly good opportunity for students who
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in AI and machine learning – from classical approaches to large language models. You are proficient in Python and key ML libraries (e.g. scikit-learn, PyTorch, LLM APIs), and you have a track record of
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optimisation or machine learning (e.g., Python/Matlab/C++; PyTorch/TensorFlow). Experience in signal processing/wireless or SDR/GPU prototyping is a plus. Demonstrated research potential is highly desirable