Postdoc in AI for Microbial Carbon Dioxide Conversion Data Framework

This postdoctoral position focuses on applying artificial intelligence techniques to develop data frameworks for microbial carbon dioxide conversion processes. The role requires expertise in AI methodologies such as machine learning, data modeling, and possibly deep learning, tailored to biological and environmental datasets. Candidates should be proficient in handling complex biological data related to microbial systems and carbon capture or conversion mechanisms. Experience with bioinformatics tools, data integration, and computational frameworks that support microbial metabolic analysis or environmental data processing is essential. Strong programming skills in languages commonly used in AI and data science, such as Python or R, and familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn) are important. The position likely demands the ability to design, implement, and validate AI models that can interpret and predict microbial behavior in carbon dioxide conversion contexts. Additionally, knowledge of microbial physiology, biochemistry, or environmental microbiology would enhance the effectiveness of AI applications in this domain. Candidates should demonstrate the capacity to work in interdisciplinary teams, integrating AI expertise with microbiology and environmental science to advance sustainable carbon capture technologies. The role is research-intensive, requiring a strong academic background, typically a PhD in computer science, bioinformatics, environmental science, or a related field, with a focus on AI applications in biological systems. Overall, the position demands a combination of advanced AI skills, domain-specific knowledge in microbial carbon conversion, and experience in developing and managing complex data frameworks for scientific research.