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Universität Berlin. The position is part of the research group QUALITY.2 (Phytonutrient Management) in the programme area ‘Plant Quality and Food Security’ (QUALITY). The aim of the research project “PhytoM” is
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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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science. A wide range of quantum theoretical methods shall be employed. A solid background in quantum mechanics and programming skills are prerequisite for this position, as is the readiness to learn and to
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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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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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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