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Field
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programming with multiple languages (e.g., Java, C/C++, Python) for geospatial information systems, agro-informatic applications, agricultural monitoring and modeling, Agro-AI/ML, or digital twin. Instructions
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Qualifications Education: ABD status by start date. PhD by start date in a field relevant to the research being conducted. PhD after one year of employment. Technical Skills or Knowledge: Proficiency in Python
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. Strong research background in biometric security, presentation attack detection, or digital identity systems. Demonstrated programming skills in Python, MATLAB, or C++, and experience with deep learning
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in at least one major programming language, such as Python, is expected Familiarity with deep learning frameworks and modern NLP toolkits is an advantage Motivation to publish research results in
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of Unix systems (GNU Linux) and keen to gain hands-on experience in Networks and systems Machine Learning knowledge is a plus Strong analytical and programming skills are required (Python, Matlab, Golang
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learning - deep neural network, recurrent neural network, LSTM Optimization theory Stochastic geometry Strong programming skills in at least one of the following: MATLAB, Python, JULIA or C++ Language
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in C++ and/or Python is expected, and experience in model analysis and parameter optimisation is beneficial. Experience in machine learning and neural networks is desirable. The successful applicant
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(especially Python) and use of ML/NLP tooling (e.g., PyTorch/Transformers, spaCy), plus fundamentals in MLOps/experiment tracking (e.g., DVC/MLflow/Git). Very good written and spoken German and English skills
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experience with new IBR models (e.g., WECC REGFMA1, REGFMB1, vendor-provided models) is a plus. Experience with software development in Python, C++, or other programming languages. Hands-on experience
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programming language (preferably Python) What we offer: A fully funded 75% doctoral position (TV-L E13) for up to 36 months • A competitive salary based on TV-L E13 for Bavaria • Enrollment as a doctoral