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-fueled engine, power converters, and a battery energy buffer. Key tasks will include developing system-level models, optimising energy management, and coordinating power flow between subsystems to maximise
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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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such as the NEPS. Potential research areas include (but are not limited to): Item response modeling of achievement tests Analysis of process data (e.g., response times) to enhance competence measurements
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susceptible steel structures. Thus, the candidate will develop reliable machine learning-based surrogate models to replace expensive phase field models to simulate failure because of HE. The activities will be
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flexibility concerning the start-date The candidate will be enrolled at TU Wien What to expect: EUR 2.786,10 gross per month (14 times / year), for 30 h / week. In addition to numerous events, seminars
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requirements. The assessor will conclude whether each applicant is qualified and, if so, for which of the two models. The assessed applicants will have the opportunity to comment on their assessment. You can
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Foundation Models initiative . The proposed starting date is 1 September 2025 or soon thereafter. The appointment will be made for a term of three years at a competitive salary and will follow the PhD study
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with a background in cognitive psychology, data science or computer science and a willingness to develop skills in computational models of cognitive processes, statistical methods, and programming (R
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-22 eV or better, and powerfully test the Standard Model of particle physics. They further constrain CP-violating new physics at scales of 10-100 TeV, far beyond the reach of the LHC. The TUM and the