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for candidates to have the following skills and experience: Essential criteria PhD in structural biology, biophysics, biochemistry, or related discipline Experience in Biological NMR and Structural Biology
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engineering, physics, applied physics, electrical engineering, chemistry, or a related field by the start date. • Hands-on research experience with crystal or thin-film growth, such as CVD, PECVD, MOCVD, MBE
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substantial experience of machine learning in a research or industry environment, and have the ability and willingness to combine machine learning research with sustained engagement with historical and
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Benefits Eligible Eligible Salary $55,000 - $60,000 annually, commensurate with experience, education, and qualifications Contract Type LOA If other, please specify Recruitment Type Open Position Details
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experiences. Embracing varying opinions and perspectives strengthens our core values which are: Respect, Excellence, Leadership, Loyalty, Integrity, and Selfless Service. Who we are As one of the fastest
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, survival analysis, longitudinal analysis, correlation analysis, clustering, dimensionality reduction, and predictive modeling. Conducts in vitro cancer biology experiments, including 2D culture, 3D organoids
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Experience Hands-on experience with mouse models and in vivo experiments Required License/Registration/Certification N/A Physical Requirements Check all that apply: ☒Sitting at computer work station for long
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| Medicinal Chemistry The lab of Dr. Liva Rakotondraibe is seeking a candidate for a post-doctoral scholar position. The successful candidate must have relevant experience in fractionation, isolation, and
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demonstrated research experience with foundation models , including the evaluation and adaptation of pre-trained models, fine-tuning strategies, and the development of new model architectures or learning
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, learning, well-being, social experiences, and decision-making. HARP is developing a longitudinal multimodal research resource to study human-AI interaction and its impacts using student-informed, privacy