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growth in a mouse model. These therapeutic agents will be evaluated alone and in combination to reduce tumor growth. Our lab has pioneered several developments in gene therapy, including being
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will focus on developing a process-guided and AI-assisted soil community foundation model for natural climate solutions. Candidates with prior experience in process-based modeling/data analytics/deep
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, including models customized for fungal genomes. Training will include crop and fungal RNA-seq analysis; integration of multi-omic and phenotypic data; protein-interaction and structure-based analyses; and
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. Developing predictive models for precision oncology: We leverage large-scale multimodal datasets, including molecular profiling, clinical records, imaging, and longitudinal treatment histories, to develop
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evaluate new reagents, technologies, and laboratory workflows for comprehensive viral detection. Developing and evaluating molecular approaches including PCR-based sequencing, targeted NGS, pan-viral
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databases. Our team studies a range of interventions, from psychotropic agents to vaccines, in relation to multiple outcomes, from pregnancy losses to neurodevelopmental disorders in the infant. We apply
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- “Functional skincare and film-forming agent”. Qualifications Applicants should have a doctoral degree or an equivalent qualification and must have no more than five years of post-qualification experience
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Multimodal Models Generative AI, Agentic AI, Physical AI, and Embodied AI Trustworthy AI, including explainability, auditability, and privacy Edge AI and model optimisation Physics-informed neural networks
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model, we were first to show that maternal obesity alone causes offspring cardiac structural defects and disrupts WNT/PCP genes and proteins in the developing heart. The successful candidate will combine
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performance using MAE, R², intraclass correlation, rank correlation, and calibration metrics Identification of organ-specific contributing proteins using SHAP values, model coefficients, and network-based