22 programming-"the"-"DAAD"-"EURAXESS"-"IDAEA-CSIC"-"Prof" Postdoctoral positions at Yale University
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leaders in science. Our mentoring plan incorporates a variety of structured initiatives and resources tailored to support career development: weekly lab meetings, individual meetings, biannual career
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science, building and evaluating tools to support students, teachers, and schools to thrive. We do this by conducting research on the science of emotions and emotional intelligence, evaluating programming
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with advanced medical and neuroscience training programs. Training: The postdoc will receive hands-on training in epilepsy research, including: Patient recruitment and consenting following IRB-approved
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, skills and experiences directly related to this position, and highlight overall research interests and plan, and (3) contact information for three professional references via Interfolio. Review
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in the design, implementation, and maintenance of data pipelines and leading/assisting in building algorithms for deep learning with close collaboration from the study team. While programming
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programs to bridge fundamental cancer biology with clinical investigation for the translation of basic discoveries into better therapeutics, treatments, or diagnoses. Compensation: Compensation
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, cellular biology and signaling, and mouse models of disease, enhancing our understanding of how the allosteric site influences physiology and disease mechanisms. We plan to utilize drug discovery to identify
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Anticipated Appointment Dates: 7/1/2025 Email/Website: Wendy.agudo@yale.edu (Program Manager) https://medicine.yale.edu/psychiatry/research/clinics-and-programs/center-science-cannabis-cannabinoids
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skills • Excellent verbal and writing communication skills. • Excellent programming skills (R or Python, Linux shell scripting) • Experience with high performance computing in Linux environment is required
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present data. The ideal candidate will have strong programming experience (R, Python, bash); working knowledge in Linux/Unix environments; experience in the analysis of large datasets; and a solid