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MSc degree in data science. Candidate Profile We are looking for highly motivated candidates interested in Software Engineering topics, and in the application of Generative AI in Software Engineering or
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charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students working collaboratively
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integration of pit thermal energy storage in thermal grids Collaboration with KTH Royal Institute of Technology on building-integrated thermal energy storage Work as an assistant teacher in relevant courses
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charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students working collaboratively
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defects and trapped charges Thermochronometry and rock surface dating You will be part of the dynamic and interdisciplinary LUMIN team, which includes engineers, scientists, postdocs, and PhD students
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bioinformatics, AI and ML software tools to integrate and process the datasets quickly and efficiently. You will also work closely with other computational and experimental biologists to uncover new insights
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process. Together, these innovations aim to make column generation more practical for solving real-world, large-scale optimization problems. These innovations will be tested within a structured software
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(CAD, e.g., SolidWorks, CATIA) and engineering (CAE, e.g., Abaqus, Ansys) software tools commonly used in additive manufacturing, structural design and related research (e.g., parametric design tools
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or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, geophysics and acoustic data modeling. A strong experience with software defined radio Automatic
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performance engineering use cases, extensive software development experience, and knowledge of machine learning frameworks (such as transformers and torch) are pluses. MSc candidates about to complete will also