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- Universitat Pompeu Fabra - ETIC
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looks to use machine learning to better control polymerization processes and products made from emulsion polymers. Applicants must have a PhD in chemistry or chemical engineering (or a related discipline
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UNIVERSIDAD CATÓLICA DE MURCIA - FUNDACIÓN UNIVERSITARIA SAN ANTONIO DE MURCIA | Spain | 23 days ago
, Databases: Oracle, SQL, MySQL Machine Learning TensorFlow scikit-learn Intepretable Machine Learning: LIME, SHAP, etc Containers: docker, singularity, etc OMICS and clinical datasets 2. English: B2-C1. 3
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The Artificial Intelligence and Machine Learning group (AI-ML) at Universitat Pompeu Fabra, Barcelona, Spain, is looking for postdoctoral researchers in the area of reinforcement learning. The AI-ML
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, programming and machine learning. Candidates should have a demonstrated record of scientific publications in international journals and participation in conferences. Experience supervising students will be
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The AI and machine learning group is seeking two postdoctoral research fellows in the area of reinforcement learning theory to work on projects related to the performance analysis of entropy
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association whose objective is to attract, retain and nurture the next generation of excellent researchers in Europe in machine learning and related topics. In addition to excellent research, ELLIS aims to have
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(statistical tools, machine learning, etc.) will be particularly valued. Interested candidates must have a PhD in Physics or Astronomy on the date of the contract start and provide a letter of interest, a CV and
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- PhD in computer science specialized in machine learning and, in particular, document image analysis, - good publication record, - strong programming skills in Python and/or C++, - knowledge and
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Depending on your skillset, you are expected to contribute to the machine learning of neural network representations for molecular potentials, improve speed in collaboration with OpenMM and Nvidia
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publications What you will do. Depending on your skillset, you are expected to contribute to the machine learning of neural network representations for molecular potentials, improve speed in collaboration with