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HBIGS Heidelberg Biosciences International Graduate School | Heidelberg, Baden W rttemberg | Germany | 5 days ago
Framework Programme? Other EU programme Reference Number DC12 Is the Job related to staff position within a Research Infrastructure? Yes Offer Description 15 PhD positions in imaging of Nuclear architecture
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processing and image representation Demonstratable experience in python for data handling and algorithm development Desirable : Knowledge and experience with imaging systems (X-ray CT, MRI or similar
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of human cohorts” (65%, Proteomics Group) Ref. Number: 396_2026 PhD Position 3 “Image-based analysis of human cohorts” (65%, Lipidomics Group) Ref. Number: 397_2026 PhD Position 4 “Data Science” (100
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large medical datasets, and also gain knowledge on how AI can be used in healthcare. The position involves many different types of meetings with other PhD students, researchers, supervisors, and
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that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase retrieval in the weak holographic regime
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. Therefore, we need an imaging scheme that captures relevant features at different length scales and integrates them into a single reconstruction volume. This PhD project focuses on learning-based phase
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diagnosis, and therapy of diseases like cardiovascular diseases or cancer. Overall, the institute strives to advance precision medicine by combining knowledge from different fields such as biology, chemistry
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. The project will then consider polymer melts up to 200 °C, relevant for industrial extrusion. Working with industrial and academic partners, you will help establish a new materials‑processing paradigm with
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large medical datasets, and also gain knowledge on how AI can be used in healthcare. The position involves many different types of meetings with other PhD students, researchers, supervisors, and
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The Computer Vision Group is looking for an aspiring PhD to investigate multi-agentic AI, LLMs, and VLMs applied to agricultural sciences. Currently, established AI models often fail to generalize