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the field of artificial intelligence with a particular focus on Public Health, e. g.: independent work on an exciting research project at the interface of AI, medicine and bioinformatics, e. g.: further
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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
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, Biochemistry, Chemistry, Bioinformatics, Physics, Pharmacy etc. and an ambitious and innovative mindset as well as excellent written and oral communication skills in English. We offer: A funded PhD position
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sciences or similar Strong interest in single-cell nucleus RNA-seq techniques / Perturb-Seq / Crop-Seq Willingness to learn computational biology/bioinformatics High motivation for scientific work and
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software engineering, computer science, data science, bioengineering, bioinformatics, engineering, physics or related Experience in either machine learning or computational biology. Interest in both
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on the topic outlined above is paramount Candidates are expected to be interested in working at the boundaries of several research domains Master’s degree in computational biology, bioinformatics, systems
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using quantitative and bioinformatic approaches to define genotype–phenotype correlations Characterisation of axonemal protein composition, ultrastructure, and motility defects of flagellar/ciliary
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with a second cycle academic degree (MSc, MD or equivalent) in biology, biochemistry, bioinformatics, biotechnology, cell biology, computer science, genetics, medicine, or other related fields, obtained
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Profile Completed Master’s degree in Biology, Biotechnology, Bioinformatics, Immunology, or a related field Strong interest in computational science and AI (prior formal training is not required, but a
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neuronal differentiation (preferred)Experience with cellular phenotyping, including lysosomal and/or mitochondrial profiling (advantage)Programming skills in R and/or PythonFamiliarity with bioinformatic