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Field
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, statistics, machine learning and deep learning. The project Motivation: Interpreting the genome means modeling the relationship between genotype and phenotype, which is the fundamental goal of biology
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related field. Documented expertise in machine learning and time-series modelling (e.g. LSTM, XGBoost, CNN). Strong programming skills in languages such as Python and R. Experience with phenotyping data
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predictive models for Alzheimerâ™s, Parkinson, and Dystonia as well as to identify novel proteins and pathways implicated on disease pathogenesis. We are currently analyzing brain, CSF and blood, multi-omic
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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analytical models Physics-informed machine learning for deformation modeling and prediction Integration of perception, planning, and control for robust real-time robotic performance Requirements Ph.D. in
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(HR+/HER2-) and aims to develop predictive models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue
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, to define novel biomarkers, and to identify novel therapeutical targets. We have pioneered in the integration of genetics with omic data to identify proteomic signatures and develop novel predictive models
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research in causal representation learning, inference, and discovery; advance explainable models that enable discovery of image-based markers predictive of future disease evolution; and build fair, robust
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machine learning and AI for clinical decision support. Develop, train, and validate predictive and explainable models using large-scale clinical registry data. Work closely with clinical collaborators
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imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal