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Field
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) equipped with a cryogenic stage for surface analysis - Develop computer code (e.g. Python) for the development and analysis of optical cavities Qualifications § PhD degree in Materials, Electrical
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and brain activity. Ongoing projects include: Large-scale naturalistic image and video datasets (including curated “visual experience” style datasets; indoor/outdoor scenes, places, objects, and actions
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representations. The project will also explore machine-learning approaches and efficient imaging strategies, including reconstruction of three-dimensional pore structures from radiography. By linking defect
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of scientific contribution, as evidenced by publications, preprints, or submitted manuscripts * Experience in coding in Python or MATLAB * Experience with in vivo calcium imaging or electrophysiology *Minimum
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: Immunohistochemistry (IHC) Immunofluorescence (IF) Digital pathology and image analysis Tissue microarray (TMA) construction/analysis Familiarity with tumour microenvironment characterisation, including immune
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related fields. Additional optional skills and qualifications: Experience in deep learning for medical imaging. Contracting requirements: Presentation of the academic qualifications and/or diplomas
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 19 days ago
supervision, publication and publicity activities, code development, and collaboration with industry, open source, and academic partners. The project goal is to develop and demonstrate a stochastic design
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, imaging approaches and/or bioinformatics. Postdoctoral Fellow – Banerjee Lab – 00399 Our lab is interested in how the brain accomplishes the ongoing transformation from sensation to action during natural
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and make particle beams more accessible for uses such as medical imaging. However, reaching very high particle energies in plasma accelerators is challenging. We are offering a postdoctoral position to
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of integrative molecular profiling and large-scale datasets (e.g. pathology images and clinical notes) to enable new discoveries across multiple patient contexts. A specific focus on advancing algorithm