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the next generation of secure agentic AI systems through cutting-edge research in adversarial machine learning and formal verification. The Role As a research scientist, you will contribute to frontier AI
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Postdocs in Generative Machine Learning for Biomedical Data. The postholders will focus on developing and applying state-of-the-art generative models (such as VAEs, GANs, and transformer-based architectures
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Teach and supervise at undergraduate, master’s, and PhD levels Collaborate with academic and industrial partners Disseminate results in top-tier conferences and journals Requirements PhD in
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, biochemical and biological compounds able to selectively generate apoptosis in primary brain tumors. ESSENTIAL REQUIREMENTS A PhD in nanotechnologies, physics, engineering, or related disciplines Previous
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Expression and validation of recombinant proteins in neuronal systems Engineering and functional testing of neuronal molecular switches Supervision and mentoring of Bachelor’s, Master’s, and PhD students
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laboratory safety and quality assurance protocols. Specific Requirements Master of Science degree in Horticulture, Biology, Plant Sciences, Agricultural Science or equivalent; and PhD in Horticulture
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-exome/whole-genome sequencing data from tumor verus normal paired DNA, as well as of data generated with RNA-seq, ChIP-seq, CUT&RUN-seq, ATAC-seq, HiC-seq, would be a plus. The candidate should have a PhD
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cancer and developing innovative biomarkers for early detection and patient stratification. ⸻ Required Qualifications • PhD in a relevant field (e.g., Molecular Biology, Genetics, Bioinformatics
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LevelPhD or equivalent Skills/Qualifications Requirements: PhD or MSc in a relevant field of interdisciplinary research (e.g. Bioinformatics or Computational Biology). R software development experience
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PhD or MSc in a relevant field of research (Bioinformatics or Computational Biology). Extensive experience in R or Python software development. Understanding of types and properties of mass spectrometry