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tools, including 4D point cloud modeling and state-of-the-art machine learning and deep learning techniques (such as generative adversarial networks), with empirical fieldwork in Norwegian glacier
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. The department has a strong community on related topics: research groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will
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country in the world. Finland and the Helsinki region possess top expertise in sciences in terms of a vibrant talent pool, leading research, strong support services, and functioning collaboration networks
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an extensive network of scientific contacts. This includes contacts to the Aalto startup scene and community. A way to be close to the research process while focusing on interesting computational problems and
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initiatives in computational design for climate-adaptive urbanism. Your network and team You will work closely with Professor Dr. Pia Fricker (https://research.aalto.fi/en/persons/prof-dr-pia-fricker ), whose
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Health Data As A Service For AI Development What we offer: A high-quality research environment. The prospects to build an international research career within a European collaborative network. A working
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communication are regulated (1-3). The successful candidate will focus on the dynamics and molecular mechanisms of the Interplanar Amida Network (IPAN) – a membrane protrusion network – using a multidisciplinary
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to build an international research career within our collaboration network, with opportunities for research visits provided. A working culture, where team members are treated as equals and open discussion is
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services, and functioning collaboration networks. For more information about working at the University of Helsinki and living in Finland, please see https://www.helsinki.fi/en/about-us/careers . How to apply
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parental leave usage across different population groups and how the use of leave has changed over time and in various social environments: workplaces, residential areas, and extended family networks