36 python "https:" "FUNDACIÓ ESADE UNIVERSITAT RAMON LLULL" Postdoctoral positions at KINGS COLLEGE LONDON
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in python programming and machine learning models for molecular discovery and chemical property prediction. For informal enquiries, please contact Dr Martin-Martinez at francisco.martin-martinez
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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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About Us The post will be based at St Thomas’ Hospital in central London in the School of Biomedical Engineering & Imaging Sciences at King’s College London: https://www.kcl.ac.uk/bmeis . There is
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relevant subject area (or pending results). Strong programming skills with proficiency in Python, R, and tools relevant to multi-omics data analysis (e.g., CellRanger, GECKO, tINIT) Demonstrated ability
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Language Processing, or a closely related field* Demonstrated experience with computational semantics models, word sense disambiguation, or semantic change detection Proficiency in Python and relevant NLP libraries
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PhD in Computational Biology, Bioinformatics, Computer Science, Statistics, or a related quantitative field * Demonstrated expertise in programming and scripting (e.g. R, Python, Bash) for data
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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 ability to work independently. Track record
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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
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experimental chemistry, providing a supportive research environment. Applicants should have a PhD in Chemistry or related field, and extensive experience in python programming and machine learning models
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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