About the Role We are seeking a Research Scientist in Computational Biology to participate in projects that leverage advanced computational techniques to analyze biological systems. In this role, you will apply biostatistics, mathematical modeling, and computational methods to solve problems and uncover insights that impact molecular and cellular medicine.
Key Responsibilities
Develop and implement computational models to analyze and simulate biological systems.
Apply biostatistical methods to process and interpret large-scale biological datasets.
Collaborate with experimental scientists to design studies and integrate computational predictions with laboratory data.
Use and develop tools for high-throughput data analysis, including genomics, transcriptomics, and proteomics.
Stay current with advancements in computational biology, machine learning, and data science to inform research directions.
Publish findings in peer-reviewed journals and present at scientific conferences.
Mentor junior researchers and contribute to collaborative projects across disciplines.
Desired Candidate Profile
Master or Ph.D. in Computational Biology, Biostatistics, Applied Mathematics, or a related field.
Expertise in biostatistical analysis and computational modeling.
Strong programming skills in Python, R, or MATLAB for data analysis and model development.
Experience with large-scale biological data types, such as genomics, proteomics, or imaging.
Proficiency in computational tools and platforms (e.g., RStudio, Jupyter, GitHub).
Excellent problem-solving and communication skills.
Preferred Qualifications
Experience with machine learning methods and their application to biological data.
Familiarity with high-performance computing (HPC) environments and cloud platforms (e.g., AWS, Google Cloud).
Knowledge of systems biology approaches, including network analysis or pathway modeling.
Background in Bayesian inference or other advanced statistical methods.
Demonstrated success in publishing impactful research.