26 high-performance-quantum-computing Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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Department of Computer Science of Faculty of Science invites applications for a POSTDOCTORAL RESEARCHER IN ALGORITHMS AND COMPUTATIONAL BIOLOGY starting from September 2025, or as agreed
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using unique novel mouse models, spatial technologies and analytical methods. Postdoctoral Researcher in Functional Cancer Microbiome through the NORPOD program NORPOD is a collaborative postdoctoral
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, including excellent skills in programming and high-performance computing Research experience in a relevant field, e.g. functional genomics, transcriptomics, bioinformatics, bioimage analysis, and/or single
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of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work provides the possibility to develop skills in
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resistance and microbiomes, statistical analysis of high-dimensional datasets, and developing bioinformatic pipelines for high-throughput analysis in high-performance computing (HPC) clusters. The work
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Application Deadline 9 Sep 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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their respective fields. About the position You are tasked with completing your doctoral thesis and your doctoral degree in accordance with the curriculum of your doctoral programme. Your duties may also include
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statistical field theory, for example, topics involving constructive quantum field theory, renormalization group theory, conformal field theory, or analysis of near critical models. Excellent written and verbal
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on personal performance. A six-month trial period will be applied. Finland is one of the most livable countries, with a high quality of life, safety and excellent education system. Finland is a member
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, calibration, and the development of analysis tools and software. Our key focus areas are the physics of jets, top quarks, and EWSB, including the development of novel machine-learning methods for high-energy