908 engineering-computation-"https:"-"https:"-"https:"-"https:"-"https:"-"L2CM" positions at Nature Careers
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• Opportunities to build a competitive academic portfolio Minimum Qualifications: • MD, DO, or equivalent medical degree (or currently enrolled in an accredited residency/fellowship program), or advanced
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, reliability, and robustness of RAI. The successful candidate must be an outstanding researcher in their field of computer science, machine learning, AI, applied mathematics or statistics with a proven track
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of Medicine (MD) programme and postgraduate programmes. The successful candidate will be expected to engage students through innovative teaching methods, fostering critical thinking, professional values and
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command of English. Key Responsibilities Develop, deliver, and regularly update content relevant to their field across the curriculum of the Doctor of Medicine (MD) programme and postgraduate programmes
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of the Doctor of Medicine (MD) programme, from pre-clinical courses to clinical rotations, as well as in postgraduate programmes. The successful candidate will be expected to engage students through innovative
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state-of-the-art computational and high-throughput experimental validation methods in functional, de novo protein design. Over the next two years, the team will jointly develop computational methods
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computer software; Assisting with preparing grant applications to secure further funding for relevant projects; Helping establish new projects and research consortia relating to the Zuccolo Group research
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of Immunocompromised Children and Adolescents beginning July 1, 2025. Interested candidates may contact: Trachia Wilson, TAGME, CAP | Fellowship Program Coordinator Pediatric Infectious Diseases | St. Jude Children's
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minimum of two years of postdoctoral experience is required. Successful candidates are expected to establish a vigorous research program and contribute to the department’s teaching mission. Applicants with
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within databases. Develop web services to query the database. Develop and implement computational and systems biology approaches for network and hidden driver analysis for genomic transcriptomics