Fakultät für Informatik - Aufgaben und Verantwortlichkeiten … Abgeschlossenes (oder kurz vor dem Abschluss stehendes) Masterstudium in Informatik, Machine Learning, Mathematik, Physik, Statistik oder einem verwandten quantitativen Bereich
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For an exciting assignment with our customer, Siemens Smart Infrastructure, we are looking for a Data Engineer (f/m/d) who is passionate about designing and operating highly scalable cloud‑native data platforms on AWS, enabling data‑driven development, and supporting …
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Wir freuen uns auf ein Kennenlernen, wenn Du über eine abgeschlossene technisch-akademische Ausbildung im Bereich Elektrotechnik, Nachrichtentechnik, Informatik, Mathematik, an einer TU/FH oder einen technisch vergleichbaren HTL-Abschluss oder eine Meisterschule … Fundiertes Interesse oder Erfahrung in …
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Automatisierung von ML-Workflows (Infrastructure as Code): Du automatisierst Bereitstellungsprozesse und ML-Pipelines mithilfe von Technologien wie Ansible oder Terraform. MLOps-Beratung und Systemadministration: Du berätst Kunden ganzheitlich an der Schnittstelle zwischen DevOps, Systemadministration und …
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Qualitative and quantitative validation of banks' internal models (rating models, IFRS 9 parameters, KYC models), with a focus on AI & ML models - Development of validation methods and programming of validation-specific applications
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Select and adapt frameworks for ML that are suitable for use in research. Practical evaluation of the selected frameworks on benchmark datasets. Continuous development of your Machine Learning knowledge and skills … Ongoing bachelor's or master's degree program in computer science, data science, physics, earth sciences …
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Master's degree/Diploma in Business Administration, Economics, Computer Science, Data Science, IT, Business Analytics, or a related field … Proven experience designing, implementing, and operating AI, ML, and advanced analytics solutions in production environments, ideally leveraging cloud-based AI platforms
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This includes satellite-based Earth Observation as well as airborne, and terrestrial laserscanning while building at the same time a strong link to already established research activities at the institute - Application fields include Ecology, Biodiversity, Monitoring, Forestry and Agriculture
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Explain insights, results, and technical concepts clearly to both technical and non-technical stakeholders … At least 2 years of applied data science / machine learning experience, ideally working on real-world problems beyond experimentation
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Explain insights, results, and technical concepts clearly to both technical and non-technical stakeholders … At least 2 years of applied data science / machine learning experience, ideally working on real-world problems beyond experimentation
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