50 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "U.S" positions at University of Basel in Switzerland
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. Fabian Baumann (fabian.baumann@unibas.ch ). You can also find out more about us at https://dg.philhist.unibas.ch/de/ . Where to apply Website https://academicpositions.com/ad/university-of-basel/2026/phd
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Baumann (fabian.baumann@unibas.ch ). You can also find out more about us at https://dg.philhist.unibas.ch/de/ . Where to apply Website https://academicpositions.com/ad/university-of-basel/2026/phd-position
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the project, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https
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, supported by Dr. Adamo and close collaboration partners, within an environment that encourages academic freedom and scientific independence. In line with our and Uni Basel values (https://www.unibas.ch/en
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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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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