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Research Engineer (f/m/d) for AI-Driven Knowledge Management for a Sustainable and Resilient Society
Ingenious Partner: Completed university degree (BSc or MSc), preferably in computer science, data science, computer engineering or similar Strong knowledge in AI/ML frameworks (e.g., TensorFlow, PyTorch) and
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Your qualifications as an Ingenious Partner: Completed master's studies in materials science, physics, metallurgy, mineralogy, mechanical engineering, or a related field. Expertise in developing
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• Supervision of BSc and MSc students This is part of your personality: • University degree in Biology, Molecular Genetics or related fields • Successful VBC PhD program Selection or similar international
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As Austria's largest research and technology organisation for applied research, we are dedicated to make substantial contributions to solving the major challenges of our time, climate change and
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. ideas for a doctoral project proposal with a CV with a list of publications (if applicable) with a proof of teaching experience (if applicable) with copies of degree certificates Please send your
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apply: with a letter of intent, incl. ideas for a doctoral project proposal with a CV with a list of publications (if applicable) with a proof of teaching experience (if applicable) with copies of degree
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to the team! It is that easy to apply: with a letter of intent, incl. ideas for a doctoral project proposal with a CV with a list of publications (if applicable) with a proof of teaching experience
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and determination? We are currently seeking a/an University assistant predoctoral -Visual Data Analysis 39 Faculty of Computer Science Job vacancy starting: 06/01/2025 (MM-DD-YYYY) | Working hours
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at the Department of Communication 49 Faculty of Social Sciences Job vacancy starting: As soon as possible | Working hours: 30.00 | Classification CBA: §48 VwGr. B1 Grundstufe (praedoc) Limited contract: 4 years
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an academic background in one or more of the FGGA discipline/s and/or Informatics resp. Data Science; ideally with experience in the areas of data management and processing, quality control, data evaluation and