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Field
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the opportunity to contribute to collaborative efforts at the interface of data science, imaging, and materials research. You will strengthen the data science and machine learning activities of the IAS-9 with
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a focus on visual language grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The
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/biomedical engineering or of relevant scientific field A solid background in machine learning Extensive experience with either computer vision or image analysis Good knowledge of deep learning packages
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adipose tissue. In particular, we will study the role of different membrane receptors and their signaling pathways in the browning process. The various techniques used will include cell biology and genetic
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concerned, among other things, with the effects of images and the mechanisms that make up communication with images. Key questions are: Who produces images and for whom? What message do these images convey
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prototype and validate your research directly in the field. Integration into a dynamic, multidisciplinary team (engineers, computer scientists, animal scientists, veterinarians). Collaborations with
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for medical imaging, tailored for deep learning. The high-level goal of the project is simple: to use anatomical knowledge and existing knowledge as training data for deep neural networks (instead of manual
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grounding, in other words, the linking of elements of natural language (words, phrases, or sentences) to visual inputs (such as images or video) in a meaningful way. The position is part of an on-going
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100%, starting January 2026 (negotiable) Proteins must fold correctly to function, and this process is tightly regulated by a network of chaperone proteins. At the heart of this network is Hsp90, a
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atoms or molecules prior to the reaction. By combining this with resonance-enhanced multiphoton ionisation and velocity map imaging, we can probe the reaction products in high detail. This powerful