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that are relevant to industry demands while working on research projects in SIT. The Candidate will be working on a sustainability and AI project, coupling physics-based models and data driven machine learning
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development of novel signal processing and machine learning methods for Brain Computer Interface (BCI)-based motor imagery kinematics decoding. Development of real-time Electroencephalogram (EEG) data
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capabilities and techniques, including natural language processing (NLP), machine learning (ML), foundational or large language models (LLM), computer vision (CV), frameworks for building and evaluating
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directed by Director/Deputy Director of CoDE. Job Requirements A good degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Masters, PhD and/or relevant professional
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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the areas of microgrid/multi-energy Operation and planning, machine learning and data analysis in the field of power system applications. Have a degree in Electrical//Electronic Engineering or relevant fields
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or Master’s degree in computer science, data science, data analytics, AI, or related fields. • Hands-on experience in machine learning, deep learning, or related AI methodologies, preferably with a focus on
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. Experienced in developing machine learning and generative AI solutions for deployment into production. Strong proficiency in Python and AI frameworks (TensorFlow, PyTorch, Keras) Strong programming skills in
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, data analytics, artificial intelligence, machine learning, etc. will be advantageous. Work independently, as well as within a team, to ensure that the project can meet the milestones adhering
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. These projects may include generative AI, AI and Machine Learning and Business Process Automation (BPA) related projects. Provide documentation support in the designing, developing, implementing, and monitoring