Use your expertise to assess the strengths and weaknesses of models, propose enhancements, and develop novel solutions to improve performance and efficiency … Design, train, and deliver custom deep learning models for our clients
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Start Date: January 2026 or as soon as possible thereafter - Aufgaben - Research in the field of computational modeling of particulate multiscale and multiphysics flows … Interest in or willingness to learn deep learning or other data-driven modeling methods
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We are seeking candidates with a methodological focus on machine learning, symbolic or sub-symbolic AI (including deep learning, reinforcement learning, generative AI, and automated decision-making), modern statistics or econometrics, optimization, simulation, and …
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Wir sind ein Start-Up aus Wien, spezialisiert auf Künstliche Intelligenz, das mit Hilfe von Neuronalen Netzen und Deep Learning verschiedenste Computer Vision Anwendungen entwickelt. Unser Ziel ist es, dem Computer das Sehen und Verstehen beizubringen
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This immersive, hands-on course offers a deep dive into advanced neural networks, state-of-the-art deep learning techniques, MLOps, and AI strategy … The course also equips participants to design and implement high-impact AI business cases tailored to Oil & Gas
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Konzeption eines Trainingsworkflow: Entwerfen eines Trainingsworkflows für Deep Learning Modelle, der alle notwendigen Schritte von der Datenvorbereitung über das eigentliche Training bis zur Modellbewertung umfasst … Laufende Ausbildung in einer HTL
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Literature research: Identification of challenges and solution approaches in the dockerization of training processes. Design of a training workflow: Design of a training workflow for deep learning models that includes all necessary steps from data preparation to the actual training and model evaluation
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Du bist die Schnittstelle zwischen Code, Daten und Architektur und wählst die technologisch beste Lösung für das Problem, egal ob Predictive Analytics, Deep Learning oder LLMs. Entwickle mit uns Ideen vom ersten Gedanken bis zur marktreifen, skalierbaren Anwendung
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Literature research: Research on existing methods and technologies of load balancing, especially in the context of deep learning evaluations. Analysis: Listing of the current challenges and limitations of existing load balancing approaches in DL evaluations
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To achieve these goals, we employ a variety of computational techniques such as machine learning and deep learning for predictive modeling and anomaly detection, statistical analysis for robust inference, and advanced visualization methods to enhance …
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