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physical). Solid background in programming and experience with machine learning. Knowledge of participatory design and co-creation methodologies. Ability to learn independently and passion for research
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Brandenburg University of Technology Cottbus-Senftenberg • | Cottbus, Brandenburg | Germany | about 16 hours ago
research topic, which is assigned to one of the research areas offered , also allows doctoral students to contribute their own institutional experience. A structured and international learning environment is
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artificial intelligence, computer science, engineering, mathematics, physics, or a related discipline Demonstratable background in machine learning, information retrieval or natural language processing
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-stage researchers in advanced data analytics, causal inference and machine learning related to health policy topics. Specifically, it is training them to evaluate real-world policy impacts. Focusing
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of the suitability of the profile for the functions and tasks to be performed focuses on the candidate's experience in machine learning and COMSOL modelling of materials and devices. Note: These criteria will be
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models with drone imagery using machine learning techniques and data assimilation. The work will involve collaboration with an interdisciplinary team of researchers, engineers, and local stakeholders in a
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systems to reason more coherently about ship designs, reducing ambiguity in the data available to machine‑learning systems, and supports explainability by grounding AI outputs in a known structure. This
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intelligence, and research software engineering, and is interested in developing robust, transparent, and sustainable computational tools. Experience with first-principles electronic structure methods, machine
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problem-based learning model. The department leverages its unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within communication, networks, control
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river valleys); • Identify stylistic patterns and regional variations in schematic rock art; • Apply machine learning tools for large-scale stylistic classification; • Establish a robust chronological