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. We care about creating a positive, respectful, and stimulating environment, valuing communication and collaboration and a workplace that promotes learning and development for all. We are committed
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are looking for a highly motivated, independent, and analytical person, with: A degree or the equivalent in scientific data analysis, data science and machine learning, computer science, bioinformatics
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data analysis, programming, and biology. You will be part of a collaborative research team with deep experimental and analytical expertise, with access to advanced tumor models and state-of-the-art
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both qualitative and quantitative methods, e.g., case studies, interviews, applied analytics, and field experiments. By developing new theories and applications, you will have the opportunity to solve
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responsibility. You are self-motivated; you pay attention to detail, and possess a problem-solving analytical ability. You are willing to help supervise bachelor and master students The ideal candidate has a
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academic background with thorough computational and analytical understanding; Proficiency in programming in Python and deep learning frameworks such as PyTorch and TensorFlow; Excellent communication skills
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of small cryptic plasmids in the development and spread of antibiotic resistance, and ii) Use machine learning tools to examine the complex interplay between bacterial hosts, various plasmids and resistance
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) biological knowledge about GRNs from bioinformatics and system biology, (b) graph theory and topological data analysis for network modeling from mathematics, and (c) robust machine learning (ML) and GenAI from
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of education, teaching and learning. Didactics includes questions about content, different educational contexts, and how content is legitimised. Furthermore, scientific didactics investigates the forms
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Technologies” is seeking four to five doctoral students in didactics. Research in didactics deals with issues of education, teaching and learning. Didactics includes questions about content, different