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background in thermodynamics and phase behavior of complex mixtures Excellent programming skills (e.g., Python, C++, Fortran, or similar) Experience with COSMO-based methods, including parameterization, model
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to network modelling, network theory and/or network meta-analyses. Fluency in programming as needed for network analyses (e.g., R/python) Strong analytical, organisational, and record-keeping skills
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. Your competencies We thus imagine that you: have a strong background in digital signal processing and machine learning; have substantial experience with scientific computing in Python/C++/ROS; know
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Quantification Python and ML frameworks (TensorFlow, PyTorch, JAX) Reproducible and open-science practices Experience with geospatial, environmental, or climate data is advantageous but not required. What We Offer
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skills with the ability to connect theoretical modeling to practical, experimental data. experience with relevant computational tools (e.g., MATLAB, Python, or similar scientific programming environments
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Neural Networks Deep Learning and Uncertainty Quantification Python and ML frameworks (TensorFlow, PyTorch, JAX) Reproducible and open-science practices Experience with geospatial, environmental
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Qualifications Ph.D. in Bioinformatics, Computational Biology, Systems Biology, or a related field Proven experience in the analysis of single-cell or spatial omics datasets Strong programming skills in Python and
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programming skills in Python and/or R Familiarity with machine learning frameworks (e.g., PyTorch, TensorFlow, scikit-learn) Excellent problem-solving, organizational, and communication skills Demonstrated
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with AI/ML implementation, particularly for sensor data processing, feature learning, or autonomous system control Solid software development skills in languages such as Python, C/C++, or similar, with
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in Python are essential, along with good writing and communication skills in English. The ideal candidate should demonstrate initiative and be comfortable working collaboratively in a team setting. A