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in machine learning, with preference for expertise in representation learning, deep embeddings, contrastive learning, or foundation models. Solid background in linear algebra, probability, and
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within the research team. Required Qualifications: Ph.D. in Chemistry, Computational Chemistry, Cheminformatics, Computer Science, or a related discipline. Proven expertise in machine learning/deep
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monitoring agricultural emissions across Africa using satellite remote sensing, atmospheric modeling, and deep learning. Research Focus Estimate cropland emissions (NH 3 , N 2 O, CO 2 , CH 4 ) using
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machine learning , deep learning , and advanced AI frameworks (TensorFlow, PyTorch, Scikit-learn). Experience with bioinformatics tools and databases (e.g., Bioconductor, Galaxy, KEGG, Reactome
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predictive maintenance in chemical plants. Key Responsibilities: Create and implement hybrid AI models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural
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have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically LLMs. The
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, Computational Chemistry, Applied Mathematics) Strong publication track record in relevant research areas Core Technical Skills (Computer Science Focus) Strong experience in Machine Learning / Deep Learning
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international journals Experience with machine/deep learning / AI applied to environmental or urban systems Familiarity with climate modeling, urban climate, urban agriculture, water resources, and