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screening pipeline, from stem-cell engineering to data analysis Develop and test improvements in single-cell methods and vector design Apply the technology in steady-state and disease settings Analyse
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On the longer term, together with other lab technicians from our team, you provide support in method development Profile You possess at the minimum a degree in Biotechnological, Biomedical or Biological Sciences
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may facilitate eligibility for certain competitive funding opportunities. Relevant experience (e.g., molecular biology, cellular physiology, neuroscience, animal behavior, computational methods, etc
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
interested in recruiting faculty members who use and develop artificial intelligence methods and mechanistic mathematical models to address fundamental questions in biology. Examples of research topics include
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We are seeking a motivated and creative PhD student to develop the next generation of AI-driven protein design methods that explicitly account for protein–lipid and protein–membrane interactions
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We are seeking a motivated and creative PhD student to develop the next generation of AI-driven protein design methods that explicitly account for protein–lipid and protein–membrane interactions
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computational approaches – including structural modelling, cheminformatics, and AI-driven methods – as part of the broader toolkit to enable program decisions, integrating computational predictions with rigorous
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environment The laboratory of Dr. Anastassia Vorobieva at the VIB–VUB Center for Structural Biology (co-affiliated with the VIB Center for AI & Computational Biology) pioneers methods for the de novo design
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environment The laboratory of Dr. Anastassia Vorobieva at the VIB–VUB Center for Structural Biology (co-affiliated with the VIB Center for AI & Computational Biology) pioneers methods for the de novo design
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, applying state-of-the-art methods to cutting-edge biomedical questions.Design, implement, and evaluate ML and foundation-model approaches, including LLM-based solutions, for scientific text mining