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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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, such as master's degree and 1-3 years of related work experience in quantitative analytical chemistry methodologies including chromatographic separations, mass spectrometry, analytical method design, use
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of Prof. Dr. Frank Cichos and Dr. Nico Scherf (Max Planck Institute for Human Cognition and Brain Sciences). The position is part of a collaborative project in the Center for Scalable Data Analytics and
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with
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for Ion Nano-Analytics (AINA) group at LIST, a research team specialized in mass spectrometry. AINA combines expertise in instrument development, nano-analytical techniques, and methodological innovation
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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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sustainability goals whilst improving operational efficiency? This PhD studentship will involve developing machine learning models, creating virtual manufacturing replicas, and implementing optimisation algorithms
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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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unique opportunity to acquire a very broad skill set of biochemical, biophysical, bioimaging and molecular techniques, combined with diverse data analysis and bioinformatics skills. The project would suit