Sort by
Refine Your Search
-
; Assess the effect of using organo-mineral resources on soil carbon stock in the field; Implement agronomic assay to evaluate the stability of clay-humic complexes under culture conditions and how
-
(especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning, particularly for multi-variable simulations. Knowledge of complex systems
-
; Implement agronomic assay to evaluate the stability of clay-humic complexes under culture conditions and how an intense of microbial activity contributes into the process of stabilization of organic matter
-
infrastructure, mobility, and energy management. Integrate real-time data from sensors and IoT devices to develop dynamic models. Model complex interactions between physical systems (infrastructure) and digital
-
chemistry, chemical biology, biochemical and thermal reactions. Research at CBS is organized around several major areas, which aim to answer challenging industrial questions, from complex chemical and
-
CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
, and Precision Health. The project aims to leverage AI and machine learning (ML) to analyze complex metabolomics datasets and address key health challenges, including biomarker discovery, disease
-
develop innovative solutions based on the analysis of urban data (big data, IoT, GIS) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing
-
Area of specialization: Critical Zone is the “heterogeneous, near-surface environment in which complex interactions involving rock, soil, water, air, and living organisms regulate the natural
-
to uncover biomarkers, therapeutic targets, and mechanistic insights into complex diseases. The project addresses critical challenges in personalized medicine, disease stratification, and multi-modal data
-
on creating and optimizing state-of-charge (SOC) and state-of-health (SOH) prediction models to ensure the safety, efficiency, and longevity of lithium iron phosphate (LFP) batteries. Key Responsibilities