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Field
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of image sensor technology through advanced pixel design Your key responsibilities include: Researching novel application-specific pixel architectures Developing hardware for characterization and application
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resonance imaging) Fluency in English Experience and knowledge: Required: Experience in computer programming Expertise in Python programming for Machine and Deep Learning, e.g., sklearn, pytorch, tensorflow
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16 Nov 2025 Job Information Organisation/Company Itä-Suomen yliopisto Department The Faculty of Science, Forestry and Technology, Department of Physics and Mathematics Research Field Mathematics
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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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engineeringEducation LevelPhD or equivalent Skills/Qualifications Educational background: PhD in Biomedical engineering, Optical Imaging/Physics, Biophotonics, Medical Imaging or AI, Image Guided Intervention or a
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a Research Infrastructure? No Offer Description Materials science and technology are our passion. With our cutting-edge research, Empa's around 1,100 employees make essential contributions to the well
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organization, safety, and protocol management (10%). -Collaborate across multiple interdisciplinary teams within the ATP-Bio Engineering Research Center and affiliated labs (5%). This position is not eligible
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property analysis, sorting, and grading. Contribute to the TIMBERHAUS project by applying advanced image processing and AI methods for timber quality assessment. Integrate experimental data with
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modern challenges in sustainability, nonlinear optics, photonic and quantum technologies, new nanostructured materials, sensing, imaging and clean energy. The group adopts an interdisciplinary approach