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Robust machine learning using information theoretic approaches for damage detection in complex machines (C3.5-ELE-Esnaola) School of Electrical and Electronic Engineering PhD Research Project
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apply machine learning algorithms with special attention to digital footprint reduction and data privacy. Functions to be developed: Develop methodologies and experiments to measure and optimize
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strong background in machine learning, computer vision, or data-driven modeling. You have extensive experience in the development and implementation of AI and machine learning algorithms, ideally with
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: Data Mining Machine Learning Bioinformatics The successful candidate will contribute to advancing state-of-the-art in data mining and machine learning research with applications in computational biology
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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/12 11:59PM ** (posted 2025/10/21, listed until 2026/04/21) Position Description: Apply Position Description
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University Medical Center Utrecht (UMC Utrecht); yesterday published | Netherlands | about 1 month ago
This is a full-time post-doctoral position (1.0 FTE), initially for 1.5 years, and will be embedded within the UMC Utrecht AI-Lab initiative. Your primary focus will be the development of AI models for defect detection and process optimization of tissue constructs (based on biomaterials and...
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Details Title Postdoctoral Fellowships in Networking Support for Machine Learning School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science Position
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position in Artificial Intelligence, Machine Learning and Software Engineering/Programming Languages. Responsibilities Design and deliver high quality courses in Artificial Intelligence, Machine Learning and
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PhD Scholarship Develop multimodal machine learning models to predict glioblastoma treatment outcomes using imaging and clinical data. Work with real-world data from John Hunter Hospital in a
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industrial Ph.D. position focused on developing scalable, Machine Learning (ML) pipelines for genomic and epigenomic biomarker discovery from Oxford Nanopore Technologies (ONT) long-read sequencing data