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of computer vision models is comfortable working in multidisciplinary research environments, where methods are applied to biological or real-world problems Qualifications The applicant must: hold a PhD in a
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 9 hours ago
campaigns and the analysis of resulting data. Principal Duties and Responsibilities Conduct independent research safely, efficiently, and professionally. Ensure that all experiments meet MIT’s safety and
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experimentation with advanced analytical tools to understand mineral-water interactions from microscopic to ocean-basin scale. Besides PI’s Kraal and Wolthers, the PHOSFLUX team consists of a NIOZ-based PhD student
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Artificial Intelligence with a primary focus on Computer Vision, pattern recognition, and visual generative modeling. CVI2 offers the opportunity to contribute to a diverse portfolio of projects, including
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and application of deep learning models from drones and satellite remote sensing data to identify urban risk areas for mosquito-borne diseases in East African cities. This position is part of an inter
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, and at the Microbial Expression Engineering Department at Novo Nordisk, Måløv. Responsibilities and qualifications As a postdoc, your primary skills will be: Design and execute experiments to identify
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, publications and experience presenting data at national and international meetings Names and addresses of 3 academic references. One of these must be your main PhD supervisor. Stanford is an equal opportunity
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FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria Knowledge / Academic Background • PhD in plasma physics, space physics, planetary science, or an equivalent discipline
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. However, progress is constrained by the scarcity of high-quality, well-characterized in vitro T-cell immunogenicity data. In this project, we will leverage expertise and technology in peptide T-cell