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scientists with a vivid interest in interdisciplinary projects to image cell dynamics from the subcellular to the patient level. PhD projects range from the analysis of basic cellular processes to clinical
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 7 days ago
programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Area of research: PHD Thesis Job description:PhD Candidate in Quantum Enhanced Microscopy (f/m/x) 102886
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Dortmund, we invite applications for the Research Group Analysis of Microscopic BIOMedical Images (AMBIOM): PhD Candidate (m/f/d) The position is part of the DFG-funded project: ComplexEye - a 96/384-well
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applications for a position as a PhD-student (M/F/D) – Protein Biochemistry and MINFLUX Super-Resolution Microscopy. At the Göttingen location of the Fraunhofer ITMP - Translational Neuroinflammation and
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activities, and a buddy programme. List of doctoral programmes and other offers We provide PhD positions within the framework of our core areas: A: Designing dynamical emergence in quantum matter B: Capturing
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Technology » Nanotechnology Technology » Measurement technology Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 31 Mar 2026 - 00:00 (Europe/Berlin) Country Germany
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Dortmund, we invite applications for the Research Group Analysis of Microscopic BIOMedical Images (AMBIOM): PhD Candidate (m/f/d) The position is part of the DFG-funded project: ComplexEye - a 96/384-well
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Your Job: The PhD project is methodologically independent and embedded in a multidisciplinary research environment at the interface of artificial intelligence, scientific imaging, and materials
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offers interdisciplinary approaches to biomedical research with a strong emphasis on imaging. PhD projects range from the analysis of basic cellular processes to clinical translation, from the generation
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-funded DECIPHER-M consortium (9 partners, €9M), we are building multimodal foundation models that integrate imaging, text, and structured clinical data to predict metastasis risk and identify tumor origin