Ph.D. student to start in Fall 2021
Assistant Professor Daniel Acuna from the School of Information Studies and the Science of Science and Computational Discovery (SOS+CD) Lab are looking for Ph.D. students to apply to the program in Fall 2021.
The SOS+CD Lab works on understanding current practices in science and developing semi-automated methods to mine scientific knowledge from vast, unstructured dataset of full-text publications, citations, and images. We use various computational techniques, including deep learning, natural language processing, graph analytics, image processing, and causal inference. The ideal student should have a good grasp of quantitative methods and be a good programmer. The ideal candidate should have an undergraduate major or masters degree in Computer Science, Engineering, Applied Statistics, Mathematics, or a similar quantitative field.
If you want to discuss your current career plans, are interested in our research, are considering a Ph.D., please send me an email to deacuna AT syr.edu.
Deadline to apply is January 3rd but you should talk to me first. More information
Assistant Professor Daniel Acuna from the School of Information Studies and the Science of Science and Computational Discovery (SOS+CD) Lab (https://scienceofscience.org) is looking for Master’s students with experience in image analysis, natural language processing, or statistical analysis.
Send an email to deacuna AT syr.edu and attach the following information:
- A short introduction of yourself and why you want to work with me
- A brief CV or a 1-page resume
- Your GitHub repository, preferably with code from a personal project rather than a “class project.”
- Your transcripts (optional but highly desirable)
- Your GRE, GMAT, or equivalent scores (optional but highly desirable)
- Develop reproducible software and tools
- Write method and result in sections for scientific manuscripts
- Have advanced computer programming skills in languages such as Python and R. SQL is also desirable
- Understand linear algebra, calculus, probability, and statistics
- Understand machine learning software tools and pipelines in scikit-learn, R, or Spark ML
- Understand basic concepts of software engineering
Job specific qualifications
- Undergraduate (for MS students) or graduate degree in Computer Science, Engineering, Applied Statistics, Applied Mathematics, or similar quantitative fields
- Minimum of 2 years of experience with coding in a major programming language such as Python, R, C, C++, or Java. Experience with handling big data with Apache Spark is a plus.
- Demonstrable knowledge of linear algebra, calculus, probability, and statistics