94 computer-science Postdoctoral positions at Conservatorio di Musica "Santa Cecilia"
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U.S. Geological Survey Climate Adaptation Science Centers (CASCs) are building the next generation of science leaders through the Climate Adaptation Postdoctoral (CAP) Fellows Program . The CAP Fellows
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are seeking a postdoctoral fellow in computational spatial biology to work in the laboratory of Professor Sylvia Plevritis in the Department of Biomedical Data Science at Stanford University. This research
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their knowledge and skillset in mitochondrial biology would best fit this position. Required Qualifications: PhD in cell biology, molecular biology, stem cell biology, developmental biology, immunology, or cancer
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Posted on Tue, 01/21/2025 - 07:48 Important Info Deprecated / Faculty Sponsor (Last, First Name): Wang, Sophia Stanford Departments and Centers: Ophthalmology Appointment Start Date: ASAP Group or Departmental Website: http://optima.stanford.edu (link is external) How to Submit Application...
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Mathematics, Physics, Engineering, or Computer Science. This is a collaborative, cross-functional team, and project assignments will be tailored to match each postdoc’s strengths and growth goals. If you are
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, Computer Science, Biomedical Engineering, or related field. • Demonstrated expertise in at least one of the following: – Large‑scale macaque electrophysiology (ideally Neuropixels) – Cognitive & behavioural
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, EBV, HBV, HPV) and chemical biology trainees with a willingness and aptitude for learning new experimental systems. Our projects require an adaptable mix of molecular virology as well as protein
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of decarbonized water and energy systems; and 3) supporting the design and enforcement of water-energy-food policies. We seek a new team member who enjoys team-science and engineering and would thrive
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excellent opportunity to work in the development of external relationships and to engage in industry-university partnerships at the cutting edge of engineering, computer and data science, technology, natural
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technology and b) large-scale data collection in a diverse sample spanning over 250 schools across 30 states to answer three significant questions regarding the mechanisms of word reading difficulties such as