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in a structured doctoral training environment. The need of microbiome research in the current age lies in joining forces from multiple disciplines to focus on understanding causal and mechanistic links
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from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised analysis of high-dimensional neural
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Biomedical Sciences, Microbiology, or related fields; experience with anaerobic bacterial culturing, mass spectrometry data analysis; experience with omics analyses and coding (e.g. python, R); experience with
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University of California, Los Angeles | Los Angeles, California | United States | about 24 hours ago
education, special education, applied linguistics, or a related field. Required • Expert knowledge of the following programming languages: Python, Javascript, HTML, SQL, R, LaTex. • Experience with
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spectrometry data analysis; experience with omics analyses and coding (e.g. python, R); experience with bacterial genetic engineering or mammalian cell culture is a plus; good project and time management skills
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consumers. You'll gain deep interdisciplinary experience—combining multiple data layers and approaches including bioinformatics, machine learning, food safety management, regulatory science, genomics and user
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expertise and supervision of experienced researchers from multiple institutes at Forschungszentrum Jülich. As one of Europe’s largest and most multidisciplinary research centers, Forschungszentrum Jülich
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science or related discipline Experience and skills Experience in developing MLIPs, including good programming skills in Python and C, demonstrated via contributions to code repositories Experience with
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distribution modelling Experience with spatial analysis and mapping tools (e.g., QGIS, ArcGIS, or spatial packages in R/Python) Interest or experience in applying AI or machine learning methods to ecological
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steelmaking technologies (e.g., Direct Reduced Iron with hydrogen) and exploring long-term transition pathways through prospective LCA. It will evaluate multiple environmental impact categories, identify