51 developer-"https:" "https:" "https:" positions at UNIVERSITY OF HELSINKI in Finland
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within the research project “From Clay to Code: Investigating Microbial Evolution in Response to Human Influence” funded by the Helsinki Institute of Life Sciences (HiLIFE). The project aims to establish a
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self-motivated, flexible, cooperative, perceive things holistically, and have a genuine service attitude and a desire to develop and take on new challenges. You have good organizational and
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on the development of environmentally sustainable technologies for farming and food production. A strong emphasis is on technology innovation and sustainability by taking into account the interaction between
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, perceive things holistically, and have a genuine service attitude and a desire to develop and take on new challenges. You have good organizational and prioritization skills as well as interaction skills. You
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microbiological and geochemical data to develop diagnostic tools for assessing hydrogen potential and sustainability of geological hydrogen production. The position offers a unique opportunity to work in a
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and the services they provide. The successful candidate will play a key role in developing and applying economic models to assess impacts of biodiversity change on ecosystem service provision
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and Correction of Industrial Design Flaws – Towards Fail-Safe Industries) is a Business Finland funded research project with the objective to develop a new type of computer-aided design platform
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artificial intelligence/geospatial AI, methods of machine learning and deep learning development of computer vision applications and image recognition methods analysis and production of big data, including
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of Helsinki offers comprehensive services to its employees, including occupational health care and opportunities for professional development. Please read more at https://www.helsinki.fi/en/about-us/careers
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning