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Field
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deployment; building research software (e.g., Python packages, database systems) and other open-source tools for computational social science research. Other potentially relevant experiences and skills include
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elements in nonlinear analysis software (preferred Atena); • Documented experience in other software for nonlinear analysis of structures; the ability to use a programming environment (e.g., Python) will
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
include: Strong foundation in one of the following areas: Machine Learning / Information Retrieval / Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date
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and Python programming skills are an advantage. Passionate about science communication and cultivating collaborative relationships with industry professionals. Excellent written and spoken English. A
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, including hands-on implementation Strong understanding of machine learning models and their development Strong analytical, problem solver, and programming skills for Python and Matlab are preferred Experience
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, MetaPhlAn, or similar). Proficiency in programming languages for data analysis (e.g., R, Python). Experience with mass spectrometry-based metabolomics approaches, including LC-MS/MS. Experience operating
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, Python, Bash). Good level on machine learning. Good level of written and oral English. Ease in a multidisciplinary environment, taste for teamwork, interpersonal skills. Scientific curiosity
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skills in Python and/or R. Experience with Google Earth Engine. Ability to work both independently and collaboratively in an international research team. Ability to write and communicate in English
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integration. Specific Requirements Required profile: - PhD in genomics, molecular biology, bioinformatics or related field. - Strong experience in NGS analyses (WES, RNA-seq). - Bioinformatics skills (R, Python
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engineering pipelines. Implement models in Python using frameworks such as PyTorch, TensorFlow. Perform statistical analyses and model benchmarking against existing methods. Collaborate with wet-lab scientists