53 python "Computer Vision Center" "Computer Vision Center" Postdoctoral positions at Aarhus University
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. Strong skills in scientific scripting (R, Python) and programming. Experience with High-Performance Computing (HPC) environments and the management/analysis of large datasets. The ability to
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-reviewed journals; collaboration in interdisciplinary and international research environments. Experience with software such as R, Python, SPSS, Stata, Sawtooth, Qualtrics or similar tools will be
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computational skills, including substantial experience with command-line environments, scripting/programming (R, Python and Bash), high-performance computing and development of reproducible analysis pipelines
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ideally with some experience of cancer research and evidence of genome editing and working with complex pre-clinical in vitro and in vivo models. Programming expertise in Python and R is desirable. As
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proficiency in English Assessment criteria and other qualifications: Experience in the Python programming language Experience in mounting micro-crystals Experience with experiments at synchrotron radiation
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programming skills, for example in Python or MATLAB. A documented publication record relative to career stage. The ability to work independently and to collaborate in an interdisciplinary team
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Trier Taasti at DCPT and the Department of Clinical Medicine. Your competences You have academic qualifications at PhD level, in medical physics, physics, biomedical engineering or computer science. It is
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and technical staff. The group is part of the Department of Chemistry and the Novo Nordisk Foundation CO2 Research Center (CORC). What we offer The successful candidate is offered: a well-developed
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, among other students and academic and technical staff. The group is part of the Department of Chemistry and the Novo Nordisk Foundation CO2 Research Center ( CORC ). What we offer The successful
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proteomics experts. You will be working primarily with mice models and primary neuron cultures. You will be involved in the Center for Proteins in Memory (PROMEMO) funded by the Danish National Research