Sort by
Refine Your Search
-
synthesis will involve wet chemistry and other well-established solid-state methods. Required qualifications: The candidate must hold a PhD degree in Materials Science, Materials Chemistry, Inorganic
-
sampling, analytical methods such as GC-MS, LC-MS, or other relevant instrumentation. Experience in olfactory system research is highly desirable. Background in experimental design, data analysis
-
research team. The successful candidate will work on optimizing methods for nanofibers extraction from various types of biomass, focusing on the development of efficient, sustainable, and scalable processes
-
compounds for use in agriculture, including their role as biostimulants, organic fertilizers, or crop protection agents. Develop and optimize methods to extract, isolate, and characterize valuable bioactive
-
systems including fault detection, diagnosis, and performance improvement methods. Mastery of dynamic reconfiguration approaches. Mastery of power converters. Mastery of software tools including MATLAB
-
algebra algorithms, including sparse/dense linear systems solving and preconditioners. Scalable implementations of linear algebra methods targeting large-scale HPC systems. Optimization of linear algebra
-
research on the development and application of spectroscopy methods such as near infrared and FTIR as alternatives to traditional methods of soil characterization, monitoring and management, Organization
-
protein expression experiments using eukaryotic or prokaryotic systems (HEK293, CHO, E. coli, etc.). Applying and optimizing transfection methods (chemical, electroporation) or viral infection techniques
-
Job description: ACER is excited to announce open rank positions for post-doctoral researchers dedicated to advancing the fields of Carbon Negative technology, Capture methods, Utilization processes
-
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