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Field
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to date focus on just one layer, understanding what keeps AF going is challenging. This PhD project aims to bridge that gap by combining advanced machine learning tools with a new experimental protocol
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, analytical and experimental techniques to investigate potential fuel additives to understand the mechanical, chemical and environmental impacts. We are looking for an enthusiastic and self-motivated person
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of computational, analytical and experimental techniques to investigate potential fuel additives to understand the mechanical, chemical and environmental impacts. We are looking for an enthusiastic and
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 7 hours ago
and cell culture techniques are desired. The candidate is expected to work closely with an interdisciplinary research team and must be motivated to acquire new experimental skills. PROJECT DESCRIPTION
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machine learning frameworks like PyTorch. The candidate should demonstrate strong analytical abilities, an eagerness to learn and solve complex problems, and possess effective collaboration skills
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critical steps of gastrulation and early development. Creating iPSC lines with mutations in elements of the GAG biosynthetic machinery. Applying novel GAG analytical technologies to investigate how changes
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validation, and regulatory reporter assays. Learn more about the group here . YOUR MISSION: Investigate how genetic variants affect male reproductive function Perform single-nucleus and bulk RNA sequencing
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biosynthetic machinery Applying novel GAG analytical technologies to investigate how changes in GAG structure and organisation drive and/or respond to shifting developmental stages. Working Environment: Based
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analysis and analytical data analysis workflows, together with other team members Implementing AI-based microscopy image analysis software as python packages Developing algorithms to deploy machine learning
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filled The overarching aim of this project is to find synergies between methods and ideas of modern machine learning and of statistical mechanics for the study of stochastic dynamics with application