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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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and Skills 1. A PhD, ScD, PharmD, MD, or equivalent degree, with experience in clinical research, epidemiology, pharmacology, or related fields 2. Strong background in epidemiological/statistical
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, computational, and machine learning/AI methods, with a particular emphasis on deep learning approaches improve our understanding and prediction of infectious disease dynamics. Projects are also strongly grounded
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; Develop new LC-MS/MS methods as necessary; Calibrate, troubleshoot, and perform routine maintenance and repair of HPLC and Mass Spectrometer instruments; Analyze and interpret results of studies, review
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methods (e.g., fluorescence, reflectance, or label-free modalities). Collaborate on development and optimization of imaging instrumentation and workflows. Perform tissue preparation, imaging, and data
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: The qualified candidate should hold a PhD in Medicinal Chemistry, Radiochemistry, Neuroscience, Pharmacology, or related fields. Hands-on experience with CNS models (e.g., neurodegenerative or psychiatric models
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experience in designing and conducting human subject research. Proficiency in statistical analysis and familiarity with software such as SAS, SPSS, R, or similar. Excellent scientific writing and oral
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equivalent (Ph.D., M.D., ScD., D.V.M., DDS etc) in an appropriate field. Applicants must have or will obtain a PhD in one of these disciplines: molecular biology, cancer biology, cell biology, genetics
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QUALIFICATIONS: - PhD in Ecology and Evolution, Virology, Bioinformatics, Computer Science or a related discipline - Demonstrated interest in computational analysis of biological data - Advanced coding skills
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requirement for completion of the didactic component of the first two years of the program. The participant's performance will be formally reviewed by the fellow's mentor as well as faculty advisors from both