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algorithms Perform data analysis, test and debug software Develop, implement and execute plans and tests Design and apply basic data sources and security practices Employ source code control techniques Assist
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machine learning by designing and developing innovative models and algorithms. Key Responsibilities: Designing and conducting comprehensive research in artificial intelligence and machine learning, with a
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Learning algorithms (clustering algorithms (K-means), genetic algorithms, and reinforcement learning) and end-user piloting of software systems – including technical attributes and legal/commercial
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algorithms as well as deep learning workflows on GPU servers (use of Git, Docker, and PyTorch) Design, implementation, and evaluation of spatial proteomics and multiplex analyses for characterizing the tumor
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learning, algorithms, and programming. Prior exposure to reinforcement learning or human-robot interaction is highly desirable, though motivated candidates with a strong grounding in AI/ML and willingness
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reducing waste, product failures and emissions. For this master’s thesis you will join our Competence Unit Complex Dynamical Systems , which focuses on the development and deployment of algorithms to control
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, design and optimize multi-sensor monitoring networks, and develop advanced detection and localization algorithms adapted to complex 3D underground geometries. The research will be conducted in close
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linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and analysis. Attack-tolerant
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PETs: This aspect requires a significant math background as it involves exploiting various mathematical results to develop a concrete cryptographic algorithm. Although desired, background in advanced
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-aware AI, HR analytics and logistics, optimisation techniques for labour and warehouse management. Experience of applying Machine Learning algorithms (clustering algorithms (K-means), genetic algorithms