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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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machine learning for audio and acoustics - Research in experimental techniques in acoustic -- - Investigación en procesamiento de señales y aprendizaje automático aplicados al audio y la acústica
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preference will be given to applicants with interdisciplinary qualifications or experience. Applicants must be teacher-scholars who will establish research agendas that involve undergraduate students and teach
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that involve undergraduate students and teach introductory engineering courses, as well as upper-level courses in their area of expertise. The position also requires engaging engineering students through active
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. You have excellent computer science skills (python, git, linux, ROS, etc.) . You have hands-on experience with machine learning frameworks such as PyTorch. You have strong analytical skills to interpret
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experimental studies, mechanistic modelling, time-resolved data analysis, and machine learning to develop and validate predictive models linking process signals to reaction behaviour, progressing from controlled
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plus Additional training in GATK, WES, RNA-seq, single-cell RNA-seq, WGCNA, machine learning applied to bioinformatics, methylation analysis, or multi-omic integration Advanced proficiency with the Linux
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, pathology and outcome data Multi-agent and predictive AI development: Develop machine-learning components for patient-trajectory modelling, recurrence and survival prediction, and integrate them
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Technology » Computer technology Technology » Communication technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 4 Oct 2026 - 23:59 (Europe/Oslo) Country
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sensors capable of characterizing the dynamic state of buildings and their inhabitants. By combining multi-microphone acquisition techniques with state-of-the-art machine learning methods, acoustic sensing