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Field
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medical professionals, scientists, and technical assistants A varied, challenging, and responsible position at the intersection of experimental and clinical research The opportunity to learn or deepen your
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machine learning approaches to quantitatively analyze experimental data and predict emergent multicellular behaviors under varying mechanical and chemical environments. For more information about our lab
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learning, as well as process-based models integrating thermal biology mechanisms. Funded by the ”Federal Ministry of Research, Technology and Space” (BMFTR), CSIDlab as part of the “Vector-borne disease
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academic assignments at the chair What we look for in you Completed master’s degree in computer science, transportation, or related engineering fields Solid background in generative AI, machine learning, and
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expertise in developing and training machine learning models (ideally with a focus on LLM), high-performance computing, data management, and software architecture Strong Python programming skills and
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interaction-rich scenarios. Ideal applicants will have a strong M.Sc. in machine learning, control, or safety, and hands-on experience with robotics. Apply now: https://lnkd.in/dNjmv835. Deadline: ASAP. We
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improvement of digital learning materials (e.g., video tutorials, exercises, guides, and documentation) in collaboration with content experts Monitoring and evaluation of the online training offer to enhance
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Your Job: In this position, you will be an active part of our "Simulation and Data Lab Applied Machine Learning". Within national and European projects, you will drive the development of cutting
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-motivation and interest to learn new skills Great to have: Experience programming in Python, Julia, or C/C++ Experience with Mathematica Experience with finite element methods, agent-based simulations, and/or
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. We are working