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Field
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models for prediction and recommendation Job Requirements: Preferably PhD in Computer Science or related field. Expertise in computer programming Knowledge in machine learning Proven research ability as
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, chromatin profiling, genomics, spatial transcriptomics and single-cell data. Apply statistical, machine learning, and network-based approaches to analyze high-dimensional biological data. Collaborate closely
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assigned by Supervisor. Requirements PhD in Computer Science or related field Expertise in computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in
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-driven reusability assessment platforms integrating NDT data, machine learning models, and RFID-enabled traceability systems. Prepare and draft technical reports, conference/journal papers, and
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learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP, BLIP) or scene-graph inference is a plus. Key Competencies Strong software development and debugging skills. Able
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or more statistical or scripting languages, preferably R or MATLAB. Knowledge of survival-based statistical analysis, such as Cox regression and Kaplan-Meier analysis. Working knowledge of machine learning
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. Develop AI, machine learning, optimisation, and decision-support algorithms. Build digital twin environments, physics-based modelling, concept evaluation frameworks, and technical risk assessments. Develop
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. Programming experience in Python, Matlab, and SQL. Mathematical modeling and data analysis. Machine/Deep learning (e.g. Pytorch, TensorFlow, JAX). City of Hope employees pay is based on the following criteria
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. Specific topics of focus include, but are not limited to, linear response, statistical limit laws, random and nonautonomous dynamical systems, spectral analysis, machine learning, data-driven dynamics
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: offshore wind turbines, wave tank experiments, fluid-structure interactions, sensors for offshore structural health monitoring, software development, machine learning and advanced AI modelling, and previous