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Field
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, hydrologic modeling, or energy systems modeling (e.g., ParFlow, WEPPCloud, agent-based models) Proficiency in scientific programming languages (e.g., Python, R) and geospatial tools (e.g., ArcGIS, QGIS
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Python programming skills. Excellent skills in written and oral English. The candidate's research proposal must be closely connected to the call and the research of MAI. Personal suitability and motivation
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to improving the quality and efficiency of the consortium code base. Preference will be given to candidates with a strong publication record and proven experience in Python programming, source code versioning
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, or laboratory automation (Python, MATLAB, or LabVIEW). PhD must have been obtained within 3 years from date of hire. Preferred Qualifications Extensive proficiency with commercial compositional reservoir
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for gene regulatory networks, single-cell multi-omics integration, spatial omics, and variant effect mapping in complex disease. Strong method/tool dev experience required (Python/R, ML/stats
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policy, economics, statistics, or a related quantitative field Additional Qualifications Strong skills in Stata, R and/or Python and experience analyzing complex data Demonstrated experience working with
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demonstrated proficiency in programming, specifically in Python and R, as well as experience with modern deep learning frameworks like PyTorch or TensorFlow. In addition to technical skills, the candidate must
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and CHIP-seq. · Expertise in downstream analysis and biological interpretation of bioinformatic findings · Proficiency in R/Python programming, developing analysis pipelines and
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., Python/Julia/C++), including experience with scientific computing tools Ability to communicate research results clearly in written and oral English Desired qualifications: Experience with reinforcement
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& simulation Bayesian networks & uncertainty quantification ML frameworks (PyTorch, TensorFlow, Hugging Face, OpenCV) & Python Automated pipelines for multi-modal data Continuous, bi-directional data flow