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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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The Network Analysis and Modelling uses machine learning to investigate how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. We are seeking a
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Environment (VTE) for disaster response simulation, integration of Building Information Modelling (BIM) with Structural Health Monitoring (SHM) using smart sensor networks, and resilience-informed design
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mobile at times; Fit and healthy – free of charge preventive medical check-ups and a wide-ranging subsidised sports programme Work-life balance – flexible working time models, plus BGM (Betriebliches
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approach combining molecular biology, genetics, genomics, epigenomics, metabolomics, biochemistry, biophysics and microscopy with bioinformatics and modelling. We work with Arabidopsis thaliana, tobacco
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Integrated fluidic, electromechanical, and optical micro- and nanosystems Biomedical engineering Biophotonics Tissue engineering Targeted modulation of cells Drug design Computational modeling & AI Research
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the training of state-space models Collaborate closely with our internal partners at PGI-14 (Neuromorphic Hardware Nodes) and international academic and industry partners Publish research articles and regular
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of the employees under nuclear law. Appropriate checks are carried out for this purpose. Perks: At the FRM II there is a flexible working time model with electronic time recording in which overtime is fully
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developing and using dedicated tools and processors Contribute to our sparse auto-differentiation libraries to accelerate the training of state-space models Collaborate closely with our internal partners
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Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 2 months ago
have hands-on experience in crop genomics and managing genotyping data. You bring first experience with biostatistics methods, e.g. with mixed-models. You are familiar with data analysis using