Sort by
Refine Your Search
-
: PhD degree in Materials Science, Chemical Processes or another suitable engineering discipline. Sound background in phosphate-based chemistry Experience in mineral chemistry Knowledge in advanced
-
of sustainable energy. We explore into renewable energy, discovering ways to maximize its potential and effectiveness. Additionally, we are committed to advancing thermal energy storage technologies, all-solid
-
of sustainable energy. We explore into renewable energy, discovering ways to maximize its potential and effectiveness. Additionally, we are committed to advancing thermal energy storage technologies, all-solid
-
guide their research work. Candidate Profile Due to the multidisciplinary character of ACER CoE, the ideal candidate must have as well a multidisciplinary scientific profile: PhD degree in Electrical
-
publications Candidate Profile: PhD in catalytic processes or equivalent in a relevant field Relevant experience in conducting catalytic tests Experience in analyzing gas-phase products using chromatographic
-
and their roles in nutrient cycling, soil health, and environmental sustainability. Job Responsibilities Conduct independent and collaborative research projects focused on the effect seaweeds
-
systems, particularly their effects on soil rehabilitation, water retention, and soil aggregation. Developing methods and protocols to assess soil health and carbon sequestration in lands restored through
-
to diverse audiences. Languages: Proficiency in both English and French, in writing and speaking. Desired Skills: Teamwork: Demonstrated ability to work effectively in a team within an interdisciplinary
-
advanced AI/ML methods for robust analysis and integration. Data sparsity, batch effects, and missing values across different omics layers and platforms. Cross-omics data fusion and representation learning
-
CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
monitoring. Reconstruction of metabolic networks and pathway analysis to understand disease-specific metabolic reprogramming. Tackling data sparsity, batch effects, and heterogeneity in clinical metabolomics