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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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following position Postdoctoral researcher (m/f/d) in Environmental Data Science and Machine Learning for the project BoTiKI Location: Görlitz Employment scope: full-time (40 weekly working hours) / part
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At the Leibniz Institute of Plant Biochemistry in the Department of Bioorganic Chemistry a position is available for a PhD in Machine Learning for Enzyme Design (m/f/d) (Salary group E13 TV-L, part
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network
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, conservation genomics, museomics, metagenomics, annotation, machine learning, Instruct users in the usage of hardware and software for molecular biodiversity research, Acquire substantial third-party funding
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modeling (FEM), and thermodynamic/kinetic simulations using tools such as Thermo-Calc and CALPHAD first experience in applying artificial intelligence and machine learning techniques to Materials Science
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Hybrid Crop Modelling Framework, integrating Process-Based Models (PBMs) with Machine Learning (ML) to enhance the accuracy and interpretability of crop yield forecasts, while evaluating key ecosystem
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of machine learning tools in cognitive and neurosciences. This is a fixed-term, three-year position. The salary is in accordance with the German public service salary scale (65 % E13 TV-L). The Embodied
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research and publications in one or more of the following areas: Item response modelling Modelling of process data (e.g., response times) for competence tests Application of machine learning methods in
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language models/machine learning Excellent written and spoken German and English Experience in museums, collections, and archives is advantageous but not required. Interested? We look forward to receiving