55 machine-learning "https:" "https:" "https:" "https:" "https:" "University of St" "St" positions at University of Basel
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predict protein-protein complementarity, design artificial protein binders, investigate the effects of mutations on protein structure and function, and apply protein representation learning to uncover
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& Machine Learning: Experience in deploying machine learning models and data science workflows in a research context (e.g., cheminformatics, predictive modelling). Design of Experiments (DoE): Knowledge
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. • Familiarity with machine learning, dimensionality reduction, clustering, and statistical modeling. • Strong communication skills, interest in interdisciplinary work, and ability to train students and postdocs.
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-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI approaches to biological questions Collaborating closely with
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1-year fellowship in Shoebill Conservation Genetics with support for a further 3-year PhD fellowship
biology, or conservation completed before 31 July 2026. In addition to an interest in evolutionary and conservation biology, the candidate should be committed to doing field work, wet-lab work, learning
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responses approximate human behavior. The project involves a collaboration between behavioral and computer scientists. The ideal candidate has some knowledge in both areas, and the specific behavioral domain
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commitment to documentation of experimental work. • Ability to work independently within a collaborative research team. • Motivation to learn new techniques and contribute to interdisciplinary research
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, computer literacy and motivation to further develop technical skills (e.g., programming), and a strong work ethic.
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research, you will teach students at the Medical Faculty, while also supervising postdoctoral researchers and PhD students. Additionally, the position includes the leadership and organization
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of novel physics-guided AI algorithms for drug design, integrating physics-based modeling with state-of-the-art deep learning methods. The project will focus on creating a next-generation docking framework