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Field
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magnetoencephalography (MEG) and behavioral tests Data analysis using Matlab or Python (speech-brain interactions, synchronicity measurements, connectivity measurements between sources) Presentation and publication
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automated manner. This includes the development of Python-based control and automation routines, as well as the analysis of large experimental datasets. These characterization techniques may be complemented
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experience with molecular simulation and scripting and/or programming languages (e.g. python) is a plus. The start date of the studentships is 1. October 2026. Due to restrictions by the funder
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/Qualifications Candidates must have a master's degree, preferably with a background in semiconductor physics. Basic knowledge of electronics and Python programming would be appreciated. Applicants must have a keen
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with data analysis/modelling and programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication
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development from spatial transcriptomics data. Activities : – design of a new mathematical method – monitoring and study of publications relevant to the field – programming/coding in Python (Pytorch
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++, Python or Matlab). Ability to work in an interdisciplinary team and interested in collaborating with industrial partners. Motivated to develop your teaching skills and coach students. Fluent in spoken and
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and microstructure-based modeling Experience with numerical methods for PDEs Programming skills in Python (knowledge of C++, Fortran or HPC is a plus) Scientific curiosity and critical thinking Ability
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modeling Programming skills in Python and machine learning packages such as PyTorch and TensorFlow Scientific curiosity and critical thinking Ability to work in interdisciplinary environments Motivation
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the University of Lleida. Responsibilities and tasks The overall aim is to develop methods and models (mainly in Python and OpenModelica) for optimization, and analysis of scale-grid systems integrated with power