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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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disciplines strong analytical and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with
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written and spoken English skills High degree of independence and commitment Experience with machine learning and high-performance computing is advantageous, but not necessary Our Offer: We work on the very
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between quantum computers (via Qiskit) and classical HPC resources Validate the QCS-MiMiC implementation on IBM’s ibm_cleveland quantum computer by reproducing recently published benchmark QM / MM
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civil/electrical/control engineering or mathematics or related study programs with a solid basis in choice modelling and/or reinforcement learning, with knowledge of MATSim is advantageous. Description
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are an advantage: femtosecond laser and diagnostics, high power lasers, ultrahigh vacuum, programming skills (Labview, Python) Ability to work closely within a team: engineers, students, postdocs and scientists, and
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breakage models, e.g. with stochastic tessellations Development and implementation of estimation methods for the model parameters, e.g. with machine learning or statistical methods Lab work and collection
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, remanufacturing, repurposing and recycling to each other for the realization of an agile network. Various machine learning approaches will be used here. Your tasks are: Requirements definition, survey and
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, their achievements and productivity to the success of the whole institution. At the Faculty of Computer Science, Institute of Artificial Intelligence, the Chair of Machine Learning for Robotics offers a full-time
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of computer simulations to refine the targeted synthesis even more and predict the self-assembly even better. Who we are The Research Training Group RTG2670 – Beyond Amphiphilicity in the second funding phase