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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
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language (e.g., C++, Python), creative approach to solving work tasks, strong motivation for research and the ability to work both independently and within an international collaboration. Contract and
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related discipline. A solid background in de novo protein design, protein structure prediction (Rosetta, AlphaFold, …), protein expression, structure elucidation, machine learning, C/C++ and/or Python with
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programming (R or Python). Advantageous: geostatistics, digital soil mapping, remote sensing, GIS, big data or cloud tools. Proactive working style, strong communication skills, and excellent English. Relevant
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Engineering, Physics or similar is required Knowledge of at least one programming language is expected: Python, Matlab, Fortran, C++, … Experience and/or keen interest in transport modeling of porous systems