37 multiple-sequence-alignment Postdoctoral research jobs at Conservatorio di Musica "Santa Cecilia" in United States
Sort by
Refine Your Search
-
-FISH Preference is given to candidates with experience in RNA-sequencing, whole exome sequencing, and associated analytical pipelines Growth mindset and ability to adopt new data analysis modalities
-
aligned with project goals and local context (e.g., household-based lead exposure surveys and/or market-based surveys). Lead sampling design, ensuring representativeness and methodological rigor. Draft and
-
mouse models. Utilize advanced technologies such as CRISPR/Cas9 gene editing, single-cell RNA- and ATAC- sequencing, single-cell spatial transcriptomics, and multiplex fluorescence imaging. Collaborate
-
test the hypothesis that the targeting of multiple mutations will enhance survival in glioblastoma. We propose to identify an amino acid substitution that can enhance proteasome processing followed by
-
Program at the Stanford Cancer Institute. She has an academic interest in Precision Medicine and her lab applies cutting-edge sequencing and imaging technologies to better understand skin cancer and rare
-
will be determined based on factors including (but not limited to) the qualifications of the selected candidate, budget availability, and internal equity. Pay Range: $86,100 Aligning Machine Learning
-
experience. Desired skills: Gene and single cell analytics experience, behavioral tests, hiPSC cell culture, stem cell differentiation, hiPSC-derivative transplant, RNA Sequencing, viral approaches, and
-
. thesis) whose research interests align with those of Professor Escobar Vega. Broadly, these include the following areas: algebraic combinatorics and combinatorial algebraic geometry. The selected candidate
-
sequence programming (ex: Pulseq) Python, MatLab, C++, etc PyTorch, TensorFlow ML Ops Required Qualifications: MD, PhD, or equivalent Technical interest & expertise in MRI Required Application Materials: CV
-
, biologics, and cannabis. Apply statistical and machine learning approaches (e.g., sequence analysis, latent class analysis, clustering) to examine medication use trajectories and patient subgroups