At our research lab, we invite researchers from various levels and disciplines to join us, as most of our projects are multidisciplinary. Our initial screening process serves as a crucial first step in gaining a comprehensive understanding of our applicant pool. This process is meticulously designed to identify candidates who demonstrate the potential to thrive in our dynamic and collaborative environment. Those who successfully pass this stage will be invited to participate in the next round of in-person interviews. These interviews will be conducted by either a researcher or a member of the DLQF administrative team, ensuring that candidates are assessed by individuals with deep knowledge of the field and the specific requirements of the role.
We take great care in evaluating each applicant’s qualifications, experience, and potential fit with our team. To support your success in this process, we strongly recommend attending our information sessions during the fall semester. These sessions provide valuable insights into the work we do at DLQF and offer an opportunity to hear directly from our team about what we look for in candidates.
We encourage you to take full advantage of these resources and prepare thoroughly for each stage of the process. We wish you the best of luck and look forward to the possibility of welcoming you to our team.
Are you a faculty member or researcher outside of Virginia Tech with a passion for multidisciplinary research in quantitative finance? The Dataism Laboratory for Quantitative Finance (DLQF) at Virginia Tech invites esteemed faculty and researchers from around the world to explore opportunities for collaboration and affiliation with our lab. This affiliation offers a unique platform to engage in cutting-edge research, collaborate with a diverse team of experts, and contribute to significant advancements in the field of quantitative finance.
Affiliation Opportunities:
- Collaborative Research: Partner with our faculty and researchers on interdisciplinary research projects that span quantitative finance, data science, AI, and related fields.
- Joint Publications: Co-author research papers and articles that contribute to leading academic journals and conferences.
- Research Seminars and Workshops: Participate in and contribute to seminars, workshops, and conferences hosted by DLQF, fostering a vibrant exchange of ideas and knowledge.
- Grant Proposals: Collaborate on grant applications to secure funding for innovative research initiatives.
- Mentorship and Guidance: Provide mentorship to graduate students and postdoctoral researchers within our lab, sharing your expertise and insights.
Affiliation Benefits:
- Access to Resources: Gain access to the extensive resources and research infrastructure at Virginia Tech, including state-of-the-art data analytics tools and computing facilities.
- Networking Opportunities: Connect with a broad network of researchers, industry experts, and academic leaders within and beyond Virginia Tech.
- Recognition and Impact: Enhance your research profile by being part of a lab known for its pioneering work in quantitative finance and multidisciplinary research.
Eligibility and Application Process:
We welcome inquiries from faculty members, researchers, and industry experts with a strong background in quantitative finance, data science, AI, and related fields.
Are you a graduate student driven by a passion for AI and quantitative finance and eager to engage in high-impact, multidisciplinary research? The Dataism Laboratory for Quantitative Finance (DLQF) at Virginia Tech invites you to join our team as a Graduate Researcher. This role offers a platform to work on pioneering projects, collaborate with experts from various disciplines, and contribute to significant advancements in the field of quantitative finance.
Responsibilities:
- Lead and participate in research projects focused on developing and refining financial models and algorithms.
- Conduct advanced data collection, cleaning, and analysis to support research objectives.
- Collaborate with faculty members, postdoctoral researchers, and undergraduate assistants on interdisciplinary research initiatives.
- Perform comprehensive literature reviews and synthesize findings to inform research direction.
- Prepare and present research reports, papers, and presentations for academic journals and conferences.
- Contribute to the mentoring of undergraduate research assistants and foster a collaborative research environment.
Required Skills and Qualifications:
- Currently enrolled in a graduate program at Virginia Tech in Data Analysis & Applied Statistics (DAAS), Computer Science, Economics, Finance, Statistics, or related fields.
- Strong foundation in quantitative analysis.
- Proficiency in programming languages such as Python, R, or C++.
- Understanding of AI and ML techniques and their applications.
- Experience with data analysis and statistical methods.
- Strong research and problem-solving skills.
- Excellent written and verbal communication skills.
- Ability to work independently and collaboratively within a research team.
- Commitment to rigorous academic research and continuous learning.
Preferred Qualifications:
- Previous experience in quantitative finance or financial technology research.
- Knowledge of financial markets, trading strategies, and risk management practices.
- Experience with data visualization and advanced statistical software.
- Record of published research in academic journals or conference proceedings.
Are you an undergraduate student passionate about quantitative finance and eager to contribute to cutting-edge research? Join our dynamic team at the Dataism Laboratory for Quantitative Finance (DLQF) as an Undergraduate Research Assistant. This role offers a unique opportunity to work alongside experienced researchers and gain hands-on experience in the exciting and multidisciplinary field of quantitative finance.
Responsibilities:
- Assist in the development and implementation of financial models and algorithms.
- Conduct data collection, cleaning, and analysis to support ongoing research projects.
- Collaborate with graduate students and faculty members on various interdisciplinary research initiatives.
- Participate in literature reviews and summarize relevant academic papers and industry reports.
- Contribute to the preparation of research reports, presentations, and publications.
- Assist in the debugging and optimization of algorithms and models.
- Attend lab meetings and contribute to discussions on research progress and findings.
Required Skills and Qualifications:
- Currently enrolled in an undergraduate program at Virginia Tech in Computational Modeling and Data Analytics (CMDA), Computer Science, Economics, Finance, Stat/Math, or related fields.
- Strong analytical and quantitative skills.
- Proficiency in programming languages such as Python, R, or C++.
- Basic understanding of AI and ML techniques and their applications.
- Ability to work independently and collaboratively in a team environment.
- Strong attention to detail and excellent organizational skills.
- Effective communication skills, both written and verbal.
- Enthusiasm for learning and staying updated with industry trends.
Preferred Qualifications:
- Previous experience in financial modeling or data analysis.
- Knowledge of financial markets and trading strategies.
- Experience with data visualization tools and techniques.