29 phd-in-computer-vision-and-machine-learning Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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-immunology-program-trimm YOUR QUALIFICATIONS We are looking for ambitious researchers with a PhD, a solid publication record, and strong background in some of the following experimental/computational areas
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achievements (half page) and research vision for postdoctoral period (half page) A complete CV, including PhD thesis title, date of defence and link to the online version of the thesis (if exists), previous and
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) Established Researcher (R3) Country Finland Application Deadline 15 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded
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: Motivation letter (max 1 page), summarising your current research experience and achievements (half page) and research vision for postdoctoral period (half page) A complete CV, including PhD thesis title, date
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- 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a
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advanced computational approaches to map the diversity, dynamics, and tissue distribution of viruses in the human body. We investigate how these viral communities influence disease susceptibility and
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resolve these questions, due to their proven ability to show plasticity in many traits. About you You have a PhD degree. You have experience with bioinformatic analysis, behavioral and/or experimental
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to the editing of the resulting volume. Teach or co-teach (up to 10% of annual workload). Contribute actively to the project and the host institution’s research community. Eligibility and assessment Applicants
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Application Deadline 31 Oct 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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, including how DNAme potentially drives trait variation and how it responds to the environment. We will use machine learning tools to perform high-throughput phenotyping of birch leaves – specifically stomatal