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are computationally expensive, making them clinically impractical at scale. The solution lies in image-based, data-driven (AI) models that learn to predict personalised fracture risk directly from
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executed, ideally involving 3D image analysis, inverse problems, or physics-informed modeling. Cryo-EM/ET and computational structural biology projects are especially relevant. Discuss results
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, and quantitative image analysis platforms. Experience with animal models of cancer, including tumor implantation, tissue collection, and surgical procedures. Experience with molecular biology
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image analysis, mouse models, or bioenergetics. Our laboratory places a strong emphasis on scientific integrity, transparency, and ethical conduct. We seek team members who value honesty, take
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, quantum computing, and machine learning Strong leadership skills Strong coding skills Ability to collaborate and establish positive interactions with a widely diverse group of people from different
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development using image-based mechanistic modelling and simulation. Project background Organogenesis, the formation of organs during embryonic development, depends on tightly coordinated cell signalling
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background You will contribute to the design and implementation of machine-learning-based sparse 3D image reconstruction modalities to be used for fast histopathological assessment of tumor biopsies with X
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and medical imaging principles. No specific medical knowledge is required. Bonus: experience coding in Python or equivalent programming language. Downloading a copy of our Job Description Full
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develops digital twins of embryonic development and reproductive health using image-based mechanistic modelling and simulation. Project background Digital twins of living systems are a hard computational
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candidate holding a master’s degree or a diploma in electrical engineering, geomatics engineering, geophysics, physics or a related field with a background in digital signal processing and/or image