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connections between the lab, classroom, and society. Required Qualifications: Highly motivated postdoctoral researcher with extensive experience with item response theory models, computer adaptive testing, and
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research at the intersection of AI/deep learning, bioinformatics, mathematical modeling, computational topology, scientific machine learning, for systems biology, single-cell genomics and protein design. The
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and experimentalists working across species as part of SCENE The Tolias Lab fuses large‑scale systems neuroscience with machine learning to derive principled models of cortical computation. Our newly
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, interns, and PostDocs at the intersection of computer vision and machine learning. The positions are fully-funded with payments and benefits according to German public service positions (TV-L E13, 100
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Expertise in machine learning, including building and deploying prediction models Strong data science coding skills in programs and languages such as Python, R, Stata, and SQL Experience with research in
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Medicine are seeking to appoint a Postdoctoral Research Fellow to join a project developing and validating deep learning computer vision models to classify mosquito breeding habitat on very high-resolution
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
professionals and biomedical researchers from all backgrounds by facilitating learning within innovative and integrated curricula and team-oriented interprofessional education to ensure a highly skilled workforce
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disease biology. Additionally, we utilize ex vivo multicellular organoid culture models and in vivo disease modeling in mice to enhance our studies. Ideal Candidate Qualifications: PhD in Biomedical Science
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ability to quickly learn and master various computer programs. Strong record of peer-reviewed publications. A PhD, MD or equivalent with prior relevant training in Immunology, Biology, Bioinformatics
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
emphasis on remote sensing. 2. Experience of using multiple sources of remotely sensed data, particularly optical, Lidar, and Radar data. 3. Sound statistical skills and use of Machine Learning/Geospatial AI