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the division of Data Science and AI , we develop data-driven methods and AI solutions that support intelligent decisions across society, advancing machine learning techniques, from foundations to industrial and
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at the Division of Biomedical Engineering Department of Materials Science and Engineering, Uppsala University Full-time temporary position for two years starting in September 2026, or as agreed upon
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of algorithms, machine learning, optimization, scientific software development and high-performance computing. The division is also an important part of the eSSENCE strategic collaboration on e-science and of
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machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models – with the support of competent and friendly colleagues in an international
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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agents Experience developing infrastructure for machine learning workflows Experience contributing to open data platforms or large scientific databases Awareness of diversity and equal opportunity issues
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are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it’s like to work and grow at KTH. Trade union representatives
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organisations, military service or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Doctoral degree should be within molecular biology