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Results 19-36 of 242
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Mobile and Wireless Data Security Course 7.5 credits
- Autumn 2026
- Växjö
- Bachelor’s level
- Half-time
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Digital Forensics Course 7.5 credits
- Autumn 2026
- Växjö
- Bachelor’s level
- Half-time
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Scientific Methods in Computer Science Course 5 credits
- Autumn 2026
- Växjö
- Master’s level
- 33%
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Selected topics in computer science Course 5 credits
- Spring 2026
- Växjö
- Master’s level
- 17%
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Selected topics in computer science Course 5 credits
- Autumn 2026
- Växjö
- Master’s level
- 17%
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Selected topics in computer science Course 5 credits
- Spring 2026
- Växjö
- Master’s level
- 17%
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Data Mining Course 5 credits
- Autumn 2026
- Växjö
- Master’s level
- 33%
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Systems modeling and simulation Course 5 credits
- Autumn 2026
- Växjö
- Master’s level
- 33%
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Project in Model-based development Course 10 credits
- Autumn 2026
- Växjö
- Master’s level
- 33%
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Parallel Computing Course 5 credits
- Spring 2026
- Växjö
- Master’s level
- 33%
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Advanced Information Visualization and Applications Course 5 credits
- Autumn 2026
- Växjö
- Master’s level
- 33%
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Project In Visualization and Data Analysis Course 10 credits
- Autumn 2026
- Växjö
- Master’s level
- 33%
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Computational and Visual Network Analysis Course 5 credits
- Spring 2026
- Växjö
- Master’s level
- 33%
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Pathway course package for the Software Technology Programme Course package 30 creditsCancelled
- Spring 2026
- Växjö
- Bachelor’s level
- Full-time
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Pathway course package for the Applied Mathematics Programme Course package 30 creditsCancelled
- Spring 2026
- Växjö
- Bachelor’s level
- Full-time
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Third-cycle (doctoral) programme in computer and information science Curious to do research in the subject of computer and information science? We offer opportunities for research for both doctoral…
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The industry graduate school Data Intensive Applications (DIA) Data Intensive Applications (DIA) is a graduate school for industrial doctoral students that focuses on applied research, addressing the…
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Doctoral project: Reuse of health data, combing the best of two worlds A method for automatic generation of features with high predictive power based on domain knowledge.