125 parallel-processing-bioinformatics Fellowship positions at Nanyang Technological University in Singapore
Sort by
Refine Your Search
-
to the programme of research. Job Requirements: PhD’s degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical, proactive and a team player Excellent
-
and to develop accurate cancer risk prediction models. This role involves developing computational pipelines, conducting statistical and bioinformatics analyses, and integrating multi-omics data
-
surgery and dissection Demonstrate experience and specialized expertise in multiphoton/ 2-photon in vivo calcium imaging and data analysis, or in gnotobiotic studies and microbiome analysis/bioinformatics
-
of invertebrate specimens (4) stable isotope analysis of leaf litter and soil samples. Findings of the study will improve our understanding of nutrient cycling processes in urban parks and forests, while also
-
Strong foundation in CFD, Programming proficiency such as Python, AI/ML techniques, Experience with parallel computing on CPU/GPU cluster, use of CUDA, MPI is a plus. Experience Experience with open-source
-
for Sustainable proteins, who will be assessing in parallel the protein digestibility, bio accessibility and bioavailability of alternative proteins Key Responsibilities: To carry out analytical biochemical
-
part of a larger network, undergo persistent changes that ultimately lead to experience-dependent rewiring of the brain. In parallel to understanding how memories are formed, we are also keen to
-
gnotobiotic studies and microbiome analysis/bioinformatics. Key Competencies/Requirements: Hold a doctoral (PhD) degree in neuroscience or relevant disciplines with strong publication track record Lead
-
Familiarity with research methodologies and ethical guidelines related to clinical and animal studies Proficiency in multi-omics data analysis, and bioinformatics, preferably preferred Excellent organizational
-
model is employed to forecast renewable energy availability, providing crucial insights for the design optimization process. The ML-assisted operation tackles the dynamic optimization of parallel energy