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electrochemical impedance spectroscopy (EIS) directly during the disassembly process to classify the cells for their reusability. A pre-trained machine learning model for assessing cell condition based on EIS data
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founded research area of "Digital Technologies" with a focus on computer-aided high-throughput methods and AI-supported model development presentation of scientific results at international conferences and
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learning models Investigation of these models in light of recent advancements in Selective State Space Models (SSMs), aiming to bridge the dynamics and working principles of SSMs with the dendrite-augmented
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essential. Good programming skills in at least one programming language (e.g., Python). Experience with machine learning, LLMs, or HCI/user study methodologies will be a plus. Strong interest in acquiring and
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novel machine learning-guided approaches. The position is located at TUM Campus Heilbronn. Your qualifications Strong background in computer science, AI, or related areas or similar fields. Solid
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Computer-adaptive methods and multi-stage testing Application of machine learning in psychometrics Predictive modeling of educational data Methodological challenges in cohort comparisons Advanced meta
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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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Description Are you interested in developing novel scientific machine learning models for a special class of ordinary and differential algebraic equations? We are currently looking for a PhD
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looking for student assistants: Leibniz-Project LAB2 (lead by Dr. Levent Neyse) and DFG-Project ‘Mental Models and Discrimination’ (lead by Kai Barron, PhD). Please note: The list of tasks and duties below
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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