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Field
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Required Related Experience R, Python or SAS, statistical analytics experience Required License/Registration/Certification None Physical Requirements Sitting for extended periods of time Shift Typically
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(e.g. R, Python) and an ability to work with large datasets Strong record of peer-reviewed publications Ability to independently design and execute experiments and interpret data Ability to work in a
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(Python), and be supported by high-quality publications. About the Institute The role will be based in the Digital Environment Research Institute (DERI), Queen Mary’s flagship University Research Institute
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field, expertise in AI/ML (e.g., PyTorch, TensorFlow, Python), interest in materials applications, and a strong publication record. The position is for two years, with the possibility of renewal, subject
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/ Knowledge Graph Representation / Recommender Systems Graph Theory/Network Science Python, and up-to-date machine learning libraries Excellent written and verbal communication skills Track record of publishing
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optimal control theory. Proficiency in programming languages (Python, MATLAB) and experience with AI/ML frameworks (TensorFlow, PyTorch, Scikit-learn). Experience in data-driven modeling, deep learning, and
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with: Ocean Colour satellite data BioGeoChemical-Argo data streams You are proficient in programming languages such as Python and/or R Experience in the field and/or the laboratory is a plus Knowledge of
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or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven ability to work independently. Track record in publishing
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, genetics, or related field • Expertise in genomic data analysis, particularly single-cell RNA-sequencing and/or alternative splicing • Strong background in bioinformatics and programming skills (e.g., Python
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-world data, with strong programming proficiency in R or Python and version control systems like Git. Familiarity with spatial and statistical libraries (e.g. INLA, PyMC, scikit-learn, GeoPandas). Proven