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Field
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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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Application Deadline 15 Oct 2025 - 23:59 (Europe/Zurich) Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Horizon Europe - MSCA Is the Job related
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available sensor and meter infrastructure, affordable computational resources, and advanced modeling algorithms. MPCs excel in handling constrained optimizations and new operational conditions, whereas RLs
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be applying methods such as sensitivity analysis, robust optimization, and stochastic modelling as you work on your project. You will be seconded with the Chalmers University of Technology (Sweden) and
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data to answer relevant questions and solve real-world problems. It brings together fundamental, methodologically driven research in optimization, machine learning, and artificial intelligence with
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the Institute of Advanced Simulation – Materials Data Science and Informatics (IAS-9) and the Institute of Energy Materials and Devices – Structure and Function of Materials (IMD-1) to establish a data-driven
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: A completed university degree (Master or equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep
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technology, positioning your career for long-term success and global scientific impact. Your primary role will be to pioneer and optimize advanced electron-beam lithography techniques to demonstrate reliable
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. This includes overseeing local plasma experiments, such as the NORTH tokamak and a linear plasma device, and contributing to the design and optimization of antennas for ion cyclotron heating and gyrotrons
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on performance and safety, for example, through the efficient computation of Lyapunov and barrier functions, forward and backward reachable sets, optimal value functions etc. The broad goal is to build upon recent