167 algorithm-development-"Multiple"-"Prof"-"Prof"-"SUNY"-"St" Postdoctoral positions at Nature Careers
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motivated candidate, who will develop and support projects at the intersection of soft matter physics and food science. The Postdoc position is part of the Food technology group at the Department of Food
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· Diverse and inclusive work environment empowering our people to fulfil their personal and professional ambitions · Gender-friendly environment with multiple actions to attract, develop and retain
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postdoctoral research position is opening to study the mechanisms underlying the response of luminal breast cancers to endocrine and cell cycle targeted therapies ● The project will involve development and
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molecular and cellular biology, biochemistry, omics, bioinformatics, electrophysiology, behavior, and multiple microscopy techniques. The hired postdoctoral researcher will receive compensation based
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studies on multiple pilot sites. • Undertake and support laboratory analysis on collected samples inc. nutritional, physiological, molecular, and food quality. • Assist with sustainability assessments inc
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profile in European IP law, ideally located near multiple European courts, including the Court of Justice of the European Union, the General Court, and the Court of Appeal of the Unified Patent Court. Your
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Technology (SIPT) Unit. At the SIPT Unit, we are committed to advancing the frontiers of science and technology through the development of innovative instruments and methodologies across multiple domains
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malignancies, such as Multiple Myeloma and Mantle cell lymphoma. To uncover vulnerabilities within the tumor ecosystem, we combine fundamental and translational approaches, working closely with the University
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in A. thaliana and wheat, which we started to functionally characterize. The Vascular Development group (https://www.derybellab.be), studies plant vascular development at the interface between
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biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and epigenetic mechanisms