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of scientific data, e.g. from image acquisition modalities or scientific simulations. Efficient algorithms are at the core of most of these data analysis and visualization applications. The focus of this Ph.D
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with machine learning and generative AI algorithms, with working knowledge of deep learning frameworks such as PyTorch or TensorFlow is considered a strong advantage. • Extensive experience in multi
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these transcripts into protein sequence databases. Guide the development of proteogenomics through implementation of novel algorithms and computational analysis infrastructure Development of tools to support clinical
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. Familiar with robotic control systems, perception, and kinematics. Comfortable working in Linux environments with tools such as Git. Knowledge of MATLAB for simulations and algorithm prototyping. Basic
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into how algorithmic systems influence the circulation of information and disinformation across digital platforms, and how such processes affect perceptions of credibility, truth, and democratic
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-of-the-art semantic and instance segmentation algorithms for 3D and 4D microtomography data. The project has a particular focus towards analysing fibre-based materials but will also consider other material
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CST Microwave Studio, HFSS or EM Pro for antenna modeling and design is required, as is experience with programming languages like MATLAB, Python, or similar for antenna array analysis and algorithm
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theoretical research, algorithm design, and the development of software tools that demonstrate the applicability of the new methods. Research environment The positions are hosted by the Department
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. The research group is part of the National Program for Data-driven Life Science (DDLS), generously funded by the Knut and Alice Wallenberg Foundation: www.scilifelab.se/data-driven/ Our group focuses on studying
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in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several