243 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "U.S" uni jobs in Denmark
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workflows Experience in quantitative data analysis and computational approaches; familiarity with machine learning or advanced statistal methods is advantageous Preferably experience with micro-CT imaging
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expected to learn new laboratory techniques and become able to use them autonomously. Furthermore, he/she will attend weekly seminars and laboratory meetings. The working hours are 37 hours per week. For
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in one or more of the languages taught at the department (French, German or Spanish). The successful applicant will strengthen the department’s focus on foreign-language teaching and learning at upper
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images. Close collaboration with the rest of our interdisciplinary team at DTU Construct and Vistacon, particularly the other postdoc position focusing on image analysis using deep learning. Please see
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within deep learning, big-data, computer vision, or related fields, as well as experience in in-line process monitoring or similar areas. Preference will be given to candidates with competence in concrete
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and daily laboratory life. You can read more about us at https://in.ku.dk/ Your responsibilities As our Laboratory Operations Manager, you will play a central role in shaping and supporting our
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objectives involving research, code development, supporting software and consulting with scientists. Other relevant qualifications (not required): Bsc in Computer Sciences. Experience with DevOps practices
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options are gamma radiation and E-beam. Regulatory agencies, including the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), strongly prefer terminal sterilization over
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university with a global learning environment, which equips its graduates with strong skills to work globally. The office is divided into four teams – and you will be placed in the MSc admissions team, which
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education within one or more of the following areas: education and democracy, education for sustainable development, school exclusion and vulnerability, special education and learning, professional formation