162 parallel-computing-numerical-methods "Fields Institute" Postdoctoral positions in Denmark
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. Strong theoretical and practical background in evolutionary genomics and population genetics, with demonstrated experience applying evolutionary-genomic methods to whole-genome data. Extensive hands
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a 2–year position from February 1 2027 or as soon as possible thereafter with possibility for extension of the employment. Job description You will be working using several methods to study RNA
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Employer Aarhus University (AU) Location Aarhus, Midtjylland (DK) Salary Competitive Closing date 30 Sep 2026 View more categories View less categories Discipline Computing , Life Science Job Type
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machine learning methods and pipelines. Qualifications Ideally, you hold a PhD in Data Sciences and have several years of experience collaborating with scientists in natural sciences, particularly
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The Department of Molecular Biology and Genetics and the Danish Research Institute of Translational Neuroscience (DANDRITE, Nordic-EMBL partnership in molecular medicine) at Aarhus University invites
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methods to whole-genome data. Extensive hands-on experience with bioinformatic analysis of whole-genome sequencing and resequencing data, including quality control, variant discovery, filtering and
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fields, approaches and content. We expect Candidates must have a relevant PhD. Commensurate with the seniority of the position applied for, the candidates must ideally have: An excellent
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Computing , Engineering Job Type Faculty Member Employment - Hours Full time Duration Permanent Qualification PhD Sector Academia Apply on website (This will open in a new window from which you will be
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site after 5 seconds) Save job Click to add the job to your shortlist Send job Job Details The Department of Molecular Biology and Genetics and the Danish Research Institute of Translational Neuroscience
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statistical methods for approaches such as model-data fusion techniques. The main focus of your position will be to develop and apply a scalable process-based modeling framework to evaluate climate-smart