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Field
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Technologies de l'Information et de la Communication Field: Telecommunications / Machine Learning / Statistical Signal Processing. Research Lab: L2S (Laboratoire des Signaux et Systèmes) Advisor: Antoine BERTHET
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critical maritime operation or system Collecting and curating operational and security-related data for AI-based threat analysis Training AI and machine learning models for anomaly and threat detection
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» Autonomic computing Engineering » Maritime engineering Technology » Computer technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 25 Apr 2026 - 23:59 (Europe
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tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in the form of graphs to analyze and predict food-effector systems. Key
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In
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(viability, proliferation, outgrowth, and invasion assays) is desirable. Experience with, or interest in, machine learning for the analysis of microscopy data and a strong ability to collaborate with
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reference. The student will focus primarily on the photonic integration of machine learning methods, contributing equally to the development of ML algorithms in this context. Their work will include
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attending an academic Bachelor’s degree in the scientific field mentioned above. Knowledge or experience (preferred) on machine learning or computer vision techniques, and interest in developing such skills
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Mathematics (Inverse Problems), Computer Science (Machine learning, Efficient Algorithms and High-Performance Computing), and Physics (Image Formation Modelling). Your project is part of the NXTGen High-tech
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Infrastructure? No Offer Description Work group: IAS-8 - Datenanalyik und Maschinenlernen Area of research: PHD Thesis Job description: Your Job: We are looking for a PhD student in machine learning to work within