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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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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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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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-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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, with proficiency in Python and deep learning frameworks like PyTorch, Hugging Face, sklearn, tensorflow. Excellent verbal and written communication skills Experience with GPU training and handling large
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results) Proficient in using R, Python or similar programming language Experience of working with clinical or equivalent data. Experience in developing analytic pipelines. Desirable criteria Experience
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with proficiency in Python, R, and tools relevant to multi-omics data analysis (e.g., CellRanger, GECKO, tINIT) Demonstrated ability to work independently, manage time effectively, and meet competing
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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