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strategies for enhancing their degradation in AD systems. Among the available analytical options, liquid chromatography with various detection modes, target and non-target mass spectrometry, and nuclear
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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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to the stipulated research field communication skills, both written and oral methodological, analytical and critical-thinking skills ability to cooperate as well as to work independently, ability to meet given
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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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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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) 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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interdisciplinary project. The project concerns algorithm design, implementations of algorithms, and simulated and biological data analysis. The student is expected to learn a bit of relevant molecular biology to