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postdoctoral researcher who will focus on Belgium and the Great Lakes Region of Central Africa. By comparing these case studies and triangulating the diverse methods, the project will enable a better
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the experimental lab (https://www.mathbioleiden.nl/software.html#virtualleaf ). Incorporate detailed insights into the model of the mechanical properties of cell walls based on experiments and small-scale
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applying for postdoctoral research funding. Who we are looking for Applicants should hold, or be close to completing by the start date, a relevant master's degree or equivalent and demonstrate the potential
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, synapse density, and neuronal network formation. The PhD thesis work will be supervised by Prof. Obermair and will be conducted within the PhD program Mental Health and Neuroscience (https://www.kl.ac.at/en
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-on experience with biological data ● Strong Python skills and experience with PyTorch or JAX, together with the statistical grounding to reason about the models you use ● A GitHub account with code you have
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, (lab) experiment, data donation), ideally already demonstrated in your thesis or other research projects you were involved in; experience with various techniques of quantitative statistical data analysis
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Awareness and Security Act, a copy of our crime statistics is available upon request by calling (631) 632-6350. It can also be viewed online at the University Police website at http://www.stonybrook.edu
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)medical or nutritional sciences.Preferably you have experience with working with large datasets in statistical software packages such as R, Python or SPSS. Proficiency in English at C1 level or higher (CEFR
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plasma and iPSC-derived human disease models. You will investigate disease-related molecular signatures using metabolomics, lipidomics, proteomics and genomics, and combine these data using statistical and
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extension for another 6 months. It is based in the Schulenburg group (https://evoecogen-kiel.de/ ). The PhD project aims at understanding how evolutionary principles can be harnessed to constrain bacterial