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Field
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Are you eager to support exciting research with ultracold atoms and trapped ions? Do you want to participate in the creation of complex machines that have never existed before? Do you enjoy sharing
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algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical optimization of QML algorithms Noise mitigation in relation
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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to optimization problems with possible topics covering: Variational quantum algorithms for optimization Quantum annealing Quantum inspired optimization Quantum machine learning with a special emphasis on classical
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learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted drug design” is led by Docent Juri Timonen
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qualification you must hold a PhD degree (or equivalent). Specifically, a PhD in manufacturing engineering (or equivalent) with documented experience in the following areas: Precision machining Machining system
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
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fields: Business Administration Computer Analytics / Artificial Intelligence Primary Education Early Childhood Education Requirements: PhD degree in the relevant field Fluency in English (teaching medium
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opportunity Experience in machine learning and deep learning Proficiency in one or more high-level programming languages such as Python, Java, or R. Strong commitment to interdisciplinary research and a track
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proficiency in relevant programming languages (e.g., Python, C++) and tools such as ROS. Experience in simulation and digital twins, as well as the use of synthetic data for training machine learning models, is