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Field
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in optimization and mathematical methods in economics Solid background in data processing and data analysis Solid knowledge of econometrics and statistical skills and strong motivation and curiosity
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related field. Strong background in yeast genetics and synthetic biology. Experience with bioreactors. Experience in the analysis of proteomic, genomic datasets (bioinformatic skills). Experience with
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 22 days ago
or chemical biology. Expertise in screening, bioinformatics, genomics, genome editing, small molecule probe development, and epigenetics is considered a plus. The working language at CeMM is English, and
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on cutting-edge projects in protein engineering, molecular biology, biochemistry, and bioinformatics under the guidance of top-tier experts. An employment contract is guaranteed for the proposed 3-year
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animals as well as access to sequencing facilities, high-end computer clusters, and an imaging and electron microscopy core facility. Research in our group covers diverse invertebrate lineages, with
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equivalent qualifications Expertise in one or multiple of the following areas: Machine Learning, Computer Vision, Image Processing, (Aerial) Robotics Excellent programming skills in Python (possibly also in C
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of bioinformatics and systems biology approaches Strong analytical thinking skills and ability to work independently as well as in an interdisciplinary team Proficiency in English is mandatory. Candidates should be
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of bioinformatics and systems biology approaches Strong analytical thinking skills and ability to work independently as well as in an interdisciplinary team Proficiency in English is mandatory. Candidates should be
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programming paradigms, languages, compilers, and software and hardware infrastructures for scientific computing to support users in the process of solving increasingly complex compute- and data-intensive
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from chem- and bioinformatics to computer vision and social network analysis. Machine learning with graphs aims at exploiting the potential of the growing amount of structured data in all these areas