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skills: Matlab, Unix, or Python (30%), Relevant publications in the project area (10%), The correspondence between the candidate's profile expressed in the letter of motivation and the requirements
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. Experience in determining environmental physicochemical parameters, namely in sediments; e. Proficiency in using QGIS and RStudio, Fortran, and Python; f. Previous professional and/or academic experience in
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building optical imaging systems; Experience developing acquisition/control software; Proficiency with optomechanical components; Programming experience in Python or C++. Non-compliance justifies exclusion
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for future climate scenarios, including proficiency in programming languages (Fortran, Python, MATLAB, and Bash), evidenced by participation in at least three studies or projects and the publication
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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
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Python, R, MATLAB, Origin, Unscrambler, SIMCA or equivalent software. • Knowledge of analytical chemistry applied to food, environmental or biological matrices, with particular emphasis on experience in
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. For admission and curricular evaluation purposes, proven skills in programming, preferably Python and/or C++; computer vision; computer graphics; real-time 3D rendering; 3D Gaussian Splatting, Mip-Splatting, LOD
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1 (one) Research Fellowship(s) for R&D Initiation under Special Regime (BII-RE) for Bachelor Student
requirements: Knowledge in Computer Science Additional optional skills and qualifications: Knowledge of Python programming, machine learning, natural language processing, databases, life sciences, or biology
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amplitude parameters; Developing Python software for the analysis, visualization, and processing of experimental data; Contributing to the implementation and optimization of data acquisition systems