Post-doctoral Fellow in Computational Biology, Bioinformatics and Neurobiology

The ideal profile of the successful applicant would be someone who: 1) has a strong training in wet-lab molecular genetics or neurobiology and has a keen interest in learning and applying computational/bioinformatic tools to analyze the data that are generated OR; 2) has a strong background in dry-lab computational biology/bioinformatics and is interested in working closely with individuals in the lab carrying out the wet lab experiments to analyze the data that are produced. The successful applicant will work independently, with consultation from the Principle Investigator (PI), to lead the data collection and analysis. Moreover, the applicant will assist in the dissemination of the data by assisting in the writing of manuscripts and presenting at scientific conferences. In addition, the applicant may be required (as directed by the PI) to work with and train graduate/undergraduate students and collaborate with internal/external research groups as needed.

Education: Applicant must hold a PhD or equivalent degree (within 3 years of being awarded) by the agreed upon start date in bioinformatics, computer science, neurobiology, molecular genetics or a related discipline.

Experience: Applicant must demonstrate a strong record of research achievement as evidenced by the ability to prepare and publish scientific manuscripts in major journals and present research findings at scientific meetings. As described above, applicants must (at minimum) have prior training in neurobiology/molecular genetics OR computational biology/bioinformatics and have a desire to work on an interdisciplinary project which merges these two areas.

Skills: For applicants with prior training in computational biology/bioinformatics: Experience in the analysis of NGS-based transcriptomic data is ideal. Familiarity with the analysis of scRNA-seq/spatially-resolved transcriptomic data is highly desirable. A strong background in the R language and the use of common R packages employed to analyze transcriptomic data would be assets. Some familiarity with Python/MATLAB/Unix/Linux environments and processing NGS data would also be advantageous.

For applicants with prior training in neurobiology/molecular genetics: Experience cloning DNA constructs, synthetic biology, primary neural cell culture, in vivo studies of the brain, immunohistochemistry/immunocytochemistry, and tissue sectioning would be assets.

In addition, regardless of prior training, the applicant must posses a number of the following skills: experience with experimental design and troubleshooting; ability to adapt and learn new techniques; strong communicator (both oral and written); excellent analytical and problem-resolution skills; good time management and organizational skills with the ability to work on multiple competing tasks.