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/fireworks/aflow). Proficiency in at least one programming language for data science and for numerical computing (e.g., Python/Julia/Fortran). We regret to inform that only shortlisted candidates will be
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large, highly diverse and multi-modal datasets (e.g., images, surveys, statistical and sensor data). Familiarity with geostatistical, GDAL, Python, PostGIS/PostgresSQL, Machine Learning, AI, Internet
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tools such as SPM, FSL, AFNI, and/or DSIStudio etc will be an advantage. Signal processing and programming skills (e.g. with Matlab, Python) will be an advantage. Ability to use statistical packages
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record is as one reference Proficiency in programming tools (e.g., Python, MATLAB, or C++) for data processing and model development is preferred. Proficiency in English and good communication capabilities
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, Physics, or any related field Strong background in machine learning, PINNs and control theory Excellent verbal and written communication skills Proficiency in programming languages in Python and/or C/C++. A
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field Strong background in control theory, optimisation-based algorithms and/or machine learning Excellent verbal and written communication skills Proficiency in programming languages in Python and/or C/C
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-based proteomics data (DDA/DIA) Familiarity with software frameworks such as DIA-NN, FragPipe, or comparable platforms Strong programming skills in Python and/or R, and solid understanding of statistical
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computational statistics or machine learning is optional but highly relevant) ● Proficiency in programming languages (R, Python, C++, Java or Julia). ● Experience with mathematical and statistical methods applied
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projects. o Strong proficiency in data science and programming (Python preferred; experience with pandas, NumPy, scikit-learn; SQL). o Practical experience with ontologies/knowledge graphs (RDF/OWL
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Experience in Linux and computational languages including R, and Perl or Python Experience in AI/ML modeling Knowledge of genetics, next-generation sequencing, genome assembly and annotation Strong oral and