933 machine-learning "https:" "https:" "https:" "https:" "https:" "Dana Farber Cancer Institute" positions in Sweden
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services can be found at: https://www.uu.se/forskning/snpseq and https://ngisweden.scilifelab.se/ We are proud to deliver high-quality data and are accredited by SWEDAC as a testing laboratory under the ISO
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quantitative or computational approaches are required. Prior experience with image analysis, machine learning, signal processing, or structural biology is meritorious but not mandatory. Excellent written and
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and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn more about our benefits and what it's like to work and grow
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Experience in groundwater modeling. Strong skills and experience with GIS platforms (e.g., ArcGIS, QGIS) and spatial data processing. Skill and experience with machine learning. Have a publication track record
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the design of antibody modalities Documented experience from cancer-related research Documented research experience in AI/machine learning/deep learning Documented experience in the development of therapies
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are meritorious As a person, you are independent and cooperative. In your work, you are structured and can prioritize tasks. You have the skills to teach others. You are aware of issues related to diversity and
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students, researchers, associate professors, and a professor. The successful applicant will also have good opportunities to learn from and socialize with other postdoctoral fellows at the department
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practical skills in the field of data science, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python
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will also have good opportunities to learn from and socialize with other postdoctoral fellows at the department. For more information about the department: https://www.slu.se/en/about-slu/organisation
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and supervise PhD students. The department has developed several courses within data science, e.g., Bayesian methods, Advanced Machine Learning, Deep Learning and AI methods. You are expected to teach