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About us At ExxonMobil, our vision is to lead in energy innovations that advance modern living while reducing emissions. As one of the world's largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for. The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society's evolving needs. Learn more about our What and our Why (https://corporate.exxonmobil.com/About-us/Who-we-are) and how we can work together (https://corporate.exxonmobil.com/Sustainability/Sustainability-Report/Social/Investing-in-people). About the Role ExxonMobil is seeking a highly motivated Postdoctoral Researcher specializing in multimodal knowledge extraction and reasoning. The successful candidate will develop advanced AI methods to extract, integrate, and reason over information from diverse data sources-including text, images, video, time series, and structured data-to support critical business and engineering decisions. This role is ideal for a recent Ph.D. graduate with expertise in multimodal machine learning, knowledge representation, and reasoning systems. The candidate will work in a collaborative environment to build next‐generation AI systems that transform complex, heterogeneous data into actionable insights. Key Responsibilities Develop methods for multimodal data fusion and representation learning across text, visual, spatial, and temporal data. Design models for knowledge extraction, including entity recognition, relation extraction, and structured information generation from unstructured and semi-structured data. Build reasoning systems that combine neural methods with symbolic or knowledge-based approaches. Develop and apply large language model (LLM)-based and multimodal foundation models for knowledge understanding and reasoning. Construct and utilize knowledge graphs and structured representations for enhanced reasoning and decision support. Enable context‐aware inference and decision‐making using heterogeneous data sources. Evaluate models for accuracy, robustness, and reasoning capability, including explainability where relevant. Collaborate with domain experts to translate extracted knowledge into decision‐support workflows. Implement scalable pipelines using modern ML frameworks and data engineering best practices. Communicate findings through technical reports, journal publications, and conference presentations. Example Research Areas Multimodal machine learning and cross‐modal representation learning Knowledge extraction from text, images, and sensor data Knowledge graphs and graph‐based reasoning Neural‐symbolic AI and hybrid reasoning systems Large language models and multimodal foundation models Information retrieval, semantic search, and question answering Temporal and causal reasoning in complex systems Applications to engineering, scientific, and industrial data environments Required Qualifications Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related field, with a focus on multimodal learning, knowledge extraction, or reasoning. Demonstrated research experience in multimodal machine learning and/or knowledge-based AI, including one or more of: Multimodal representation learning Information extraction or natural language understanding Knowledge graphs or structured representations Reasoning systems (neural, symbolic, or hybrid) Experience with modern deep learning architectures, including transformers and foundation models. Strong programming skills in Python. Hands‐on experience with machine learning frameworks such as PyTorch, TensorFlow, or JAX. Experience working with heterogeneous datasets (text, images, structured data, etc.). Strong analytical, problem‐solving, and communication skills. Ability to work effectively in multidisciplinary teams. Preferred Qualifications Experience with multimodal foundation models or large language models (LLMs). Familiarity with knowledge graph construction, querying, and reasoning frameworks. Experience with retrieval‐augmented generation (RAG) or hybrid search systems. Background in probabilistic reasoning, causal inference, or uncertainty‐aware AI. Experience with scalable data pipelines and distributed ML systems. Experience applying AI methods to scientific, engineering, or industrial datasets. Strong publication record in multimodal AI, NLP, or knowledge‐based systems. Self-improvement prop #J-18808-Ljbffr