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, compromise privacy, or circumvent alignment safeguards. In this project, the PhD student will investigate the protection of AI models at runtime, addressing multiple stages of the inference pipeline
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coupled with CRISPR gene editing. We use state-of-the-art sequencing methods e.g. Oxford Nanopore long-read sequencing and 10X genomics single-cell sequencing. Moreover, we will work with TE tailored
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of next-generation sequencing (NGS) datasets, such as RNA-seq, ChIP-seq • Microscopy techniques, including confocal and fluorescence microscopy • RNA biology and gene expression Eligibility Doctoral
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and how it aligns with your qualifications. It must also include your resume, degree certificates or equivalent, and any other supporting documents you wish to submit (copies of transcripts, contact
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letter explaining why you are interested in the position and how it aligns with your qualifications. It must also include your resume, degree certificates or equivalent, and any other supporting documents
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, sequencing or metabarcoding Experience with bioinformatic or statistical analysis, for example in R. Documented experience with scientific writing, for example participation in a scientific publication or
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(flotation, McMaster, or Baermann technique). Experience with advanced molecular analyses such as qPCR, sequencing or metabarcoding Experience with bioinformatic or statistical analysis, for example in R
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registered in a database. Specimens will also be collected for later DNA-sequencing and molecular work. The work will be carried out within the framework of the research group “Population biology, micro- and
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biodiversity. Subject area Data-driven evolution and biodiversity concerns research that takes advantage of the massive data streams offered by techniques such as high-throughput sequencing of genomes and biomes
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where recent advances – such as within sequencing, AI, imaging, mass spectrometry, tomography and exposomics provide new opportunities. Main responsibilities Research and some teaching and supervision