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/ machine learning / statistics on spatial and single-cell omics (transcriptomics, proteomics, epigenomics, metabolomics, meta-transcriptomics, etc.) data. Independently carry out computational and
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Details Posted: Unknown Location: Salary: Summary: Summary here. Details Posted: 06-Mar-26 Location: Cambridge, Massachusetts Categories: Academic/Faculty Computer/Information Sciences Internal
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areas. This fellowship places a strong emphasis on the application of machine learning, artificial intelligence, and bioinformatics to solve complex biological problems. Potential research activities may
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas
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to £43,805 per annum We welcome applications from skilled, delivery-focused engineers to contribute to our successful railways-applied smart machines research programme. About the Role We are seeking a highly
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machine learning, computer vision, and medical image analysis, with publications in top-tier AI and medical image analysis conferences and journals, including CVPR, ICCV, ECCV, NeurIPS, MICCAI, TPAMI, TIP
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP
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Biology, or a related field Solid foundation in cellular immunology or molecular biology Strong motivation to learn CAR T cell technologies and translational research approaches Excellent organizational
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including spectral flow cytometry, machine learning and irradiation techniques to generate bone marrow chimera models. Utilize mouse models and patient-derived samples to explore how biological immune aging
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related