Looking for highly motivated PhD students for Computational Biology research, with an algorithm development focus. The Ecological and Evolutionary Signal-processing (EESI) and Informatics lab is doing a restart from the pandemic and will be composed of a dynamic, interdisciplinary team. The project that the student will support will be designing semi-supervised clustering and classification techniques.
Also, there is an emphasis on algorithm efficiency analysis and optimization — so great programming skills are required. In addition, the student will be investigating deep learning architectures to improve DNA/RNA identification and microbiome analysis. Former lab members have gone on to work in prestigious data science jobs in industry, medical research labs, and one at a tenured position at a university. A PhD in EESI will easily lead to a fruitful career in data science development and research at top notch institutions.
What’s important to the EESI Lab:
* Curiosity — we are passionate about the possibilities of personalized medicine via microbiome manipulation and individual genomics
* Responsibility — We have an obligation to the community to conduct ethical research and communicate clearly with stakeholders
* Agility — flexibility and a desire to be nimble, smart, and effective
* Follow-through — we’re building a diverse team in a multidisciplinary environment
Position Requirements:
• B.S. in Computer Engineering, Electrical Engineering, Computer Science, Biomedical Engineering, or related discipline like Physics/Math
• Problem-focused
• Independent and able to drive tasks to completion
• Proficiency in Python
• Basic UNIX/Linux knowledge but willing to learn
* comfortable with the Linux command line interface (pipelines, sed, grep, awk, etc.)
* some familiarity with command line development tools: make, cmake, git
Preferred Skills:
• Masters of Science in a related field
• Experience with PyTorch/Tensorflow/Keras and/or machine learning implementations
• Machine Learning theory
• Experience with programming (C/C++)
• Experience with distributed computer architectures and working with containers
• Knowledge of (GP)GPU programming and CUDA
• Familiarity with building open source software (GNU-style configure and make, etc.)