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imaging and in vitro/in vivo tracking. A central objective of the project is to understand the fundamental mechanisms governing nanoparticle formation and drug compartmentalisation during PISA. To achieve
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. Application of Bayesian mixing models to investigate consumer diets and food web pathways Requirements To meet the general entry requirements you must have been awarded a second-cycle (Master’s) qualification
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chromatographic techniques. Integration and synthesis of previous data with newly collected data on fatty acids, cyanotoxins, and stable isotopes. Application of Bayesian mixing models to investigate consumer diets
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 2 months ago
covers the object's entire life cycle and uses real-time data sent by sensors on the object to simulate its behavior, monitor operations, and anticipate its functioning. Partial differential equation (PDE
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objective is to develop methods that move beyond correlation-based prediction toward causal reasoning, intervention-aware modelling, and interpretable AI systems. This transition from correlation to causation
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incorporate methods that integrate: - Mendelian randomization and genetic instruments - Bayesian hierarchical models and Gaussian graphical models - Multi-layer data integration across tissues and omics
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proficiency in oral and written English, creativity, thoroughness, and a structured approach to problem-solving Additional qualifications Experience with one or more of the following areas is meriting: Bayesian
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reinforcement learning (RL), active learning, Bayesian decision theory, and stochastic optimisation for partially observed and evolving systems. Key research directions include: adaptive data acquisition
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, including reinforcement learning, hierarchical models, Bayesian inference etc. More details of key responsibilities of this role, in addition to the essential and desirable job criteria, are available in
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ecology. A strong quantitative mindset is essential, including good skills in data analysis using R, Python or similar tools. Experience with trophic ecology and Bayesian approaches would be an advantage