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an annual budget of 17 MCHF and about 170 researchers and collaborators, Idiap is today a key Swiss research institute in multiple AI fields, including machine learning (including deep learning, foundation
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. They provide high-quality, impactful, and inclusive experiences that deliver exceptional learning outcomes. Capella values the scholarly-practitioner model and expects Assistant Professors to continue learning
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punctuality are critical to this position. Ability to quickly shift priorities as needed and instructed to by staff. 4. Strong computer skills with the ability to quickly learn new systems. Strong working
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includes, but is not limited to: photonic data processing and communication systems; AI-driven optical signal processing and network optimisation; machine learning for photonic systems modelling, control
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Ability to lead and work in teams Essential Application/Interview Experience and capability in blast computational simulations using codes such as Viper:: Blast, machine learning, and/or LS Dyna Desirable
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: undergraduate or post-bac research experience in animal physiology Certificates/Credentials/Licenses NA Computer Skills Word and Excel software knowledge, willingness to learn lab specific software as needed
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on the research strengths in bioengineering, data analytics, artificial intelligence, and machine learning. More information on our research strengths can be found at https://www.uta.edu/academics/schools-colleges
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modelling knowledge, incorporate reliability/uncertainty, and/or explainable models. The position is in the Digital Signal Processing and Image Analysis Group, Section for Machine Learning, Department
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Your Job: Investigate current challenges and bottlenecks in power flow analysis for large scale electrical distribution grids Apply machine learning/AI or surrogate modeling (e.g., neural networks
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, certification, and/or registration. Specific knowledge, skills and abilities required to perform the job satisfactorily include: Demonstrated experience developing and/or implementing machine learning models in