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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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for cosmetic products Development and application of machine learning and data analysis methods What you contribute Ongoing enrollment in food chemistry or food technology, chemistry, physics, engineering
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and verifiably safe. We consider resilience and intelligence to be part of the same process. Be part of change In this master thesis, you will work at the intersection of multimodal machine learning
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to design and control biological systems in a targeted manner. An integral part of BioSysteM is the AI Core Unit, a cross-institutional support unit. This unit aims to make machine learning (ML) and
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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conventional machining, assembled with the provided cells, and evaluated on the eMAST test bench under vibration and climate conditions as well as in a driving cycle including charging and discharging. The cells
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Basic knowledge in at least one of the following areas: integrated photonics, optical communications, machine learning/neural networks, or numerical simulation of optical systems Programming experience in
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biophysics, computational biology, mathematics in the life sciences, computer science and machine learning with application to biological systems, and related areas. What we seek The ELBE program seeks
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looking for a PhD candidate to work on research at the intersection of machine learning, data privacy, and medical imaging. The position is part of a three-year research project that investigates how