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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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, data science, quantitative social sciences, or a related discipline. Experience in developing models and mapping with real world data, with strong programming proficiency in R or Python and version
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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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and written communication skills, including academic conference presentations and journal papers; Excellent mathematical and programming skills in Python or C++, and practical experience with deep
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conference presentations and journal papers; Excellent mathematical and programming skills in Python or C++, and practical experience with deep learning libraries (e.g., PyTorch) Desirable criteria Research
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optical paths set up and design for research. Excellent communication skills and reporting High level of commitment and working in a team Desirable criteria Coding ability (labview, Maltab, Python
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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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, or related discipline Strong background in machine learning and deep learning, particularly in generative models (VAEs, GANs, transformers) Programming experience in Python and deep learning frameworks
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, GANs, transformers) Programming experience in Python and deep learning frameworks (PyTorch) Knowledge of medical image processing and analysis techniques Ability to understand and implement mathematical