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Postdoctoral position on machine learning in quantum chemistry at the Computational Science Lab (...
to the machine learning of quantum chemistry calculations using neural network potentials and enhance capabilities in terms of representing longer range physical terms, charges, etc. You will also work together
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and Machine Learning for data fusion. - FPGA-based acceleration of AI/ML algorithms. The PhD candidate will be part of a highly international and enthusiastic team. This 3-year position will be funded
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. Physiological Signal Analysis with wearable devices. Affective Computing. Deep-learning and Machine Learning for data fusion. FPGA-based acceleration of AI/ML algorithms. The PhD candidate will be part of a
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Postdoc Position on “Data-driven Optimization/Machine Learning in Energy” The group OASYS (“Optimization and Analytics for Sustainable EnergY Systems”) is looking for highly-talented applicants
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Commission › EURAXESS › Jobs & Funding › Postdoctoral Fellow on Machine Learning for Energy Forecasting EURAXESS Toggle navigation Jobs & Funding More Working environment Charter & Code for Researchers
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the Forschungsverbund Berlin (https://www.fv-berlin.de/ ) and a member of the Leibniz Association www.leibniz-gemeinschaft.de . We are offering a PhD Student or Postdoc Position (m/f/d) for the topic: “Machine learning
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: Development of new biomarkers of cancer progression, drug resistance and metastasis using machine learning techniques.We are expecting that the candidate will lead research projects, collaborate with biologist
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. EDUCATION: Undergraduate in molecular biology or similar. PhD in molecular biology, chemistry or similar. More that 3 years of post-doc experience, preferably with animal models. Valid license to work with
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in the above fields. Proven expertise in data analysis and statistical modelling or machine learning. Advanced musical knowledge. Experience in development with Python. Knowledge on MusicXML
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sector. The objective is to research in the applications of Machine Learning techniques and advanced time series models for forecasting the behavior of electricity markets, physical systems and production