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learning (ML) methods—including surrogate modelling, feature extraction, and inverse design algorithms Generate synthetic microstructures (based on the open-source OptiMic software) Perform descriptor
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proteins Dario Riccardo Valenzano - TE Landscapes in African Killifish: Evolutionary Origins, Diversification, and Activity During Aging Claudia Waskow - Mechanisms of immune aging in mice - histone
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motivation in conducting evolutionary and chemical ecological research at different levels. •MSc/diploma in a relevant field (e.g., evolutionary biology, chemical ecology, sensory biology). •Strong experience
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Dortmund, we invite applications for a PhD Candidate (m/f/d): Analysis of Microscopic BIOMedical Images (AMBIOM) You will be responsible for Developing new machine learning algorithms for microscopy image
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molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. Importantly, mastery of the experimental and theoretical aspects shall equip doctoral
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learning and data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms
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microscopy and atom probe tomography will be prepared. Finally, you will merge the images by means of deep learning algorithms. Your tasks in detail Development of the experimental protocol for the imaging
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, Microbiology, Evolutionary Biology or equivalent Practical experience with bacterial cultivation techniques (familiarity of BSL-2 biosafety protocols) Proficient in working under sterile conditions Ideally
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populations from New Guinea using museomic data Assembling high-quality reference genomes and generating whole-genome resequencing data of avian skins Inference of evolutionary history using Ancestral
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Degree Doctoral degree in biology, neuroscience, ecology, evolutionary genetics Doctoral degree or degree awarded by Ludwig Maximilian University of Munich and Technical University of Munich Course