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Field
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, enhanced with machine learning. Key responsibilities will include: • Leading the design and implementation of the Bayesian inverse model for pancake-ice detection (in collaboration with project
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for this position, the following is required: PhD in data or computer science, machine learning, AI, statistics, mathematics, biophysics, bioinformatics. Additional requirements In addition to your CV and your
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 1 month ago
conditions: Scientific area: Bachelor’s Degree in Systems Engineering. Work plan: Model-based digital twins supported by physics-informed machine learning for multiphysics modeling. 1. Grant duration and
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optimization modeling. An ideal candidate would use their foundational knowledge of various modeling methods to develop and apply cutting-edge machine learning and data analysis techniques for assessing and
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to behavioural and population health data relevant to Singapore’s diverse communities. Develop predictive and explainable machine learning/deep learning/genAI models using multimodal cross-section and longitudinal
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, United States of America [map ] Subject Areas: Electrical and Computer Engineering / artificial intelligence , Artificial Intelligence and Machine Learning (AI/ML) Starting Date: 2026/01/01 Salary Range: $62,232-$80,000
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/ machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and
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of induced pluripotent stem cell-based human disease models 2) Understanding the role of immunity during tumorigenesis as well as disease relapses in order to design novel inhibitors, CAR-T cell therapy and
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Development and training of AI/machine learning models Real-time validation of algorithms to ensure reliable physiological monitoring under real-world conditions The role offers the opportunity to contribute
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at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model reduction, with