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applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based estimation from images and text metadata. Build and evaluate monocular depth pipelines
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3T Siemens MR scanners, OPM-MEG, EEG, eye tracking, and TMS laboratories. They will also have access to Princeton's world-class computational infrastructure, including GPU systems capable of running
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Job Description Field of Research: Computational biology/bioinformatics with emphasis on spatial transcriptomics, proteomics, and immunosequencing using Pixel‑seq and an immune receptor‑focused
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computation running on NVIDIA GPUs, and is being developed both for use within the Moore Lab and for broader adoption by the global neuroscience research community. What we provide: A team that believes in
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techniques such as yeast display and deep mutational scanning, or computational candidates with experience in generative AI, reinforcement learning, or agentic AI. The lab is supported by world-class
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data from the European XFEL facility at DESY. Project website: https://www.mpinat.mpg.de/628848/SM-Ultrafast-XRay-Diffraction Your profile Eligible candidates have strong skills in computational
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fields, applying advanced techniques such as large-scale data processing and GPU-accelerated computing. Access to state-of-the-art research facilities and a new GPU cluster. Collaborative and inclusive
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diverse scientific domains, pushing the boundaries of AI to solve some of the most pressing scientific challenges. With access to competitive computational resources, we offer an exciting multidisciplinary
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physics, mathematics or any related field. What we offer State of the art on-site high performance/GPU compute facilities Competitive research in an inspiring, world-class environment A wide range of offers
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tasks: You will work together with renowned astrophysicists and computer scientists in the DFG-funded “Dynaverse” Excellence Cluster You will invent, implement, and benchmark novel AI tools (reinforcement