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Field
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-of-the-art methods, datasets, and challenges Proven experience with: Video data processing for learning and inference Deep learning architectures for video analysis Python programming and PyTorch framework
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, Python, Mplus, or similar) - Familiarity with big data, Linux systems, and remote computing clusters - Experience with genome-wide statistical genetics approaches (e.g., GWAS, genetic correlations, PGS
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(essential) extensive experience in programming in Python or Matlab and data analysis (essential) experience with behavioural and neuroimaging (fMRI, M/EEG) data design/collection/analysis (essential) track
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and data processing skills: experience of programming in one or more languages (e.g. R, C/C++, Python, Matlab). Practical experience of algorithm development and implementation of machine learning
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Geosciences, Environmental sciences, Civil or Environmental Engineering, Physics or Mathematics or a related discipline Experience in programming (e.g., Python, MATLAB, or similar), interest in machine learning
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programming or scripting skills (e.g., Python, MATLAB, Bash) are a plus Prior experience in amyloid biology, protein aggregation, or disease-related structural research are an advantage. Ability
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focus on quantum impurity models (particularly Kondo model). • Strong computational skills (with Python or Julia or C++ or Matlab or equivalent) and using numerical techniques such as exact
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simulation and enhanced sampling techniques. Candidates must be proficient in Python and familiar with version control platforms such as GitHub. You should demonstrate a strong research track record through
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date). Strong background in power systems analysis (OPF, state estimation) and numerical optimization/control. Proficiency with Python/MATLAB and power-system toolchains (e.g., MATPOWER/OpenDSS
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strong background in shape modelling, deep generative modelling (diffusion/transformers), or multimodal representation learning. You have strong programming skills, especially in Python, and preferably