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técnicas de machine learning e IA. Sólidos conocimientos en análisis y tratamiento de datos, programación y desarrollo de modelos analíticos. Experiencia con bases de datos y herramientas de análisis
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modelling (e.g., LSTMs) and deep generative/unsupervised anomaly detection techniques (e.g., VAEs). *Strong programming proficiency in Python and familiarity with standard data science and machine learning
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mechanisms associated with immune phenotypes, as well as development and application of machine-learning and statistical models for classification, prediction, feature selection, dimensionality reduction
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concepts from nonlinear system identification, optimisation, computational complexity, and data-driven modelling, with the long-term objective of extending these ideas to modern AI and machine learning
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM
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, predictive modeling, machine learning, and causal inference methods. Experience with claims-based or EHR-based phenotyping, variable construction, treatment pattern analyses, healthcare utilization studies
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management of sensitive research data Experience applying machine learning, deep learning, computer vision, time-series or sequence modeling, or multimodal data fusion to person-level driving, safety, health
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microscopy. • Expertise in metabolomics with mass spectrometry is desired. • Strong general computer skills, experience with databases and scientific applications, and ability to quickly learn and master
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Models Basic Qualifications: A Ph.D. or equivalent degree in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong
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to the mathematics of plasmas, with a focus on kinetic theory and PDEs. - Specific topics include stability, long-time behaviour, and mathematical properties of magnetised plasma models (such as magnetized