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analysis, including time-domain, spectral, and statistical analysis; Programming experience in C/C++ and Python; Knowledge of development in an embedded Linux environment; Experience producing scientific
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analyses; Familiarity with mass spectrometry data and microbial identification; Experience with Python for data science. EVALUATION CRITERIA The selection will be based on the following criteria: 50
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(e.g., Python, Fortran, and task automation in a Linux environment) (25%); ii) scientific output (e.g., published scientific articles, conference proceedings in the field of air quality) (25%); iii
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of digital signal processing and data analysis; Knowledge of applied mathematics, statistics, and estimation; Knowledge of scientific programming in Python and/or MATLAB; Programming experience in C/C
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; • Experience in data analysis and visualization. Minimum requirements: • Experience in Python; • Knowledge of optimization algorithms; • Knowledge of modelling or simulation. 5. EVALUATION OF APPLICATIONS AND
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project research; - to build or help build scripts (Python or equivalent) to trace the spread of the aforementioned vocabulary in early modern print; - build up a taxonomy of early modern prisca-related
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the following tasks: - to build up a full ‘prisca’ vocabulary in Latin, English, and at least one other language included with the project; - building or help building scripts (Python or equivalent) to trace
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and identify green skill gaps within the prison context. Phase 2: Data Modelsling and Structuring: Utilize data analysis techniques (e.g., Python and statistical modelling) to process information
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curricular evaluation purposes, proven skills in programming, preferably Python; machine learning and artificial intelligence; language models and retrieval-augmented generation (RAG); ASR/TTS; diarisation
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, Transformers, Python, PyTorch and/or TensorFlow, multimodal fusion); Active and semi-supervised learning; Software engineering for R&D (Git, reproducible environments, large-scale data pipelines). Exclusive