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Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
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; Specialized in NLP, text mining, and graph analytics, including the use of NLTK, spaCy, TextBlob, VADER, and transformer-based models (e.g., BERT, GPT); Familiar with social network analysis (SNA) using
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experienced in managing research projects with tools like Git/GitHub, ClickUp, and Jupyter Notebooks; Specialized in NLP, text mining, and graph analytics, including the use of NLTK, spaCy, TextBlob, VADER, and
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, and Jupyter Notebooks; Advanced in NLP, text mining, and graph analytics, including the use of NLTK, spaCy, TextBlob, VADER, and transformer-based models (e.g., BERT, GPT); Familiar with social network
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Notebooks; Expert in NLP, text mining, and graph analytics, including the use of NLTK, spaCy, TextBlob, VADER, and transformer-based models (e.g., BERT, GPT); Expert in social network analysis (SNA) using
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, reinforcement learning, simulation, and counterfactual analysis. Multimodal NLP & Fusion, large language models (LLMs), cross-attention Fusion, vision-language transformers. Ontological engineering, knowledge
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such as Education and Technology (EdTech), Human-Computer Interaction (HCI), Natural Language Processing (NLP), or Applied Artificial Intelligence. Demonstrable projects (e.g. GitHub repositories
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Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), particularly in Natural Language Processing (NLP) and Computer Vision (CV) Familiarity with genomic and bioinformatic databases
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experience will suffice for research engineers. Applicants with skills in Video Analytics, NLP (Natural Language Processing) or any of the following would be preferred: Caffe, Spark ML, Keras, PyTorch
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. Applicants with skills in Video Analytics, NLP (Natural Language Processing) or any of the following would be preferred: Caffe, Spark ML, Keras, PyTorch, TensorFlow, Scikit- Learn, CNTK. Also Big data