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development as well as field data collection at multiple test sites in Ethiopia. Job assignments The successful candidate will join a large, collaborative team of researchers with expertise in electromagnetic
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Postdoc position: mechanisms of autoimmunity & autoinflammation in inborn errors of immunity (m/f/d)
to decipher key regulators of human immune homeostasis (Ardy RC et al., Gastroenterology 2018; Wang L et al., Nat Genet 2021; Salzer E et al., Science Immunol 2020; Block et al., New Engl J Med 2023
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contribute to dynamics of neurodegeneration and this project will aim to refine our understanding of cellular mechanisms, biomarkers and genetic risk of amyotrophic lateral sclerosis (ALS), frontotemporal
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to publications and grant writing, and support the supervision of students and junior researchers in one the following research areas: Computational oncology AI drug discovery Statistical genetics, single-cell
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within biomedical science. The department is among the largest of its type in Europe and has especially strong research profiles in psychiatric, cancer, reproductive, pediatric, pharmaco, genetic, and
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twins together with two PhD students, especially to propose new models and algorithms for complex maneuvers, and building a parametric autonomous model of drivers reproducing a close to reality human
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Aarhus University invites applications for a postdoc position at the Department of Molecular Biology and Genetics. The project focuses on applications on de novo protein design and its applications
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at Baylor University is seeking multiple postdoctoral research associates with expertise in either organic chemistry or chemical biology. Please see below for specific information about desired candidate
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with