38 multiple-sequence-alignment Postdoctoral research jobs at Conservatorio di Musica "Santa Cecilia"
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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
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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
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, 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
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. 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
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. The postdoctoral scholar will be primarily in charge of the large animal model development and leveraging this model to evaluate the technologies developed by the other team members, although there will be multiple
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stakeholders, ensuring the research remains impactful and aligned with real-world needs. Finally, the postdoc will participate in disseminating research findings through presentations, talks, and publications
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cell fate decisions, particularly during early neural development or during the epithelial-to-mesenchymal transition (EMT) in cancer. Our recent work reveals that coding sequences (CDS) and their cognate
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and multi-omics data analysis, and a high-performance computing environment (Unix/Linux) is highly preferred. An individual with Next generation sequencing experience is preferred. A good understanding
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settings and has the ability to work independently with minimal supervision Experience working in interdisciplinary settings that combine multiple perspectives and disciplines, ideally across topics
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an ambitious and motivated postdoctoral scholar to join an exciting project focused on a unique alignment of visual, acoustic, and structural data for a radical new assessment of anthropogenic impact on our