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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
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Science, Computer Science, Applied Mathematics and Statistics, Electrical and Computer Engineering, Biomedical Engineering, or a related field. Experience with a deep learning framework like PyTorch. Strong
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”. Are you excited about the opportunities that data science in nutrition—especially the role of targeted metabolic modelling, machine learning and AI offer in developing effective personalized nutrition
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healthy and tumor-bearing animals using machine learning and AI approaches; and (3) integration of PBPK and QSAR models with AI methods to develop AI-assisted computational approaches to support decision
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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sequencing, analysis of proteins and enzymes, histological analysis including immunohistochemical techniques. Technical expertise in computer data bases and statistical analyses. Demonstrated proficiency in
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Intelligence, Machine Learning, Data Science, Electrical Engineering, or a related field. Strong experience in developing and applying AI/ML models to energy systems or similar applications. Proficiency in
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Michael Bronstein, AITHYRA Scientific Director AI and Honorary Professor of the Technical University of Vienna in collaboration with Ismail Ilkan Ceylan, expert in graph machine learning, invites
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well as collaborate with PhD candidates. You'll design experiments using our robotic platform, analyze tactile data patterns, implement real-time control algorithms, and validate performance across diverse scenarios
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familiarity in machine learning (ML) and artificial intelligence (AI). This role is pivotal in evaluating the economic competitiveness of the U.S. in the production and manufacturing of energy-related materials