872 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" positions in Sweden
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working conditions and attractive benefits. Equality, diversity and equal opportunities are essential to quality and form an integral part of KTH’s core values as a university and public authority. Learn
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the start. The position is a fixed-term appointment of four years, with the possibility to teach up to 20%, which extends the position up to five years. A starting salary of 34,550 SEK per month (valid from
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to teach on the undergraduate/master’s level. The position is meritorious for future roles in academia, industry, or the public sector. Contract terms Full-time temporary employment for a maximum of two (2
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/tensions between the global North and global South. We will also consider applicants focused primarily on Swedish/Nordic cases or topics. For full information of the five REMESO research streams see: https
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. The position bridges machine learning and molecular science, with opportunities for collaboration, mentorship, and impactful research. About us The Department of Computer Science and Engineering (CSE
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support the teaching activities courses at KTH and further develop methodologies and algorithms for the quantum computer simulators. Qualifications Requirements A graduate degree or an advanced level
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) , was created. The Department of forest genetics and plant physiology is part of Umeå Plant Science Centre (UPSC, https://www.upsc.se ) which is a centre of excellence for experimental plant research and forest
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Sweden’s national environmental objectives. Read more about the department: https://www.slu.se/ekologi Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work
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employees and is characterised by a friendly, inspiring and international atmosphere, with a large number of employees from all over the world. The PhD student will be included in Machine- and Materials
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Initiatives in Forest Research (WIFORCE) program. The successful applicant will work on the development of bioacoustic monitoring methods using automated recording units (ARUs), deep learning methods, and