1,578 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "Imperial College London" positions in Germany
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contribute Completed an excellent doctorate in computer science Proven track record of excellent publications Expertise in cryptography and/or machine learning Leadership capabilities and ability to mentor
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profile is shaped in particular by five high-profile areas. About Minds, Media, Machines Minds, Media, Machines (MMM) is one of the five interdisciplinary, high-profile areas that largely define
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team — comprising biologists, engineers, computer scientists, and medical researchers — develops next-generation computational models to interpret complex biomedical data across multiple scales. Our
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sensor data, machine learning, and privacy-first architectures come together in healthcare. Be part of change As CDO, you own CAELIA’s data and AI backbone. Your success will be measured by how well our
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the course of the application process pursuant to Art. 13 of the General Data Protection Regulation of the European Union (GDPR) at https://portal.mytum.de/kompass/datenschutz/Bewerbung/. By submitting your
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packages for machine learning. You are not afraid of learning new technologies and frameworks and enjoy developing prototypes. You are independent, creative and curious and you can work independently. What
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the provisions of data protection law. Further information on data protection can be found in our data protection regulations at https://www.tu-braunschweig.de/datenschutzerklaerung-bewerbungen . Application costs
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. Responsibilities include: Managing and mentoring a multidisciplinary team of machine learning and image analysis experts Coordinating DCU activities across partner sites and aligning them with HI strategy Co
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verschiedene Projekte in Bereich E-Mental Health durch (weitere Informationen unter https://www.sg.tum.de/digitalhealth/startseite/ ) The position is suitable for disabled persons. Disabled applicants will be
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, organization, compression, analysis, and visualization of georeferenced or geometric data in large scales. We put emphasis on methods of distributed computing, machine learning, image and text analysis