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Opportunities Database

Faculty, Post-Docs, and Graduate Students: Click here to post a research opportunity.

NOTE: If you are eligible for Federal Work-Study (FWS), you can find hundreds of research opportunities on the FWS website. To find out if you are eligible or if you are new to having a FWS award, visit the UNC FWS website. If you are a returning student who already completed the mandatory training and has access to JobX, log in and click “Find a Job” under the student menu. From there, click the “Research Jobs” button in the middle of the page.

Students with or without FWS can use the database below to look for opportunities.


Undergraduate Research Assistant Opportunities at (MURGE Lab) on Large Language Model Reasoning/Uncertainty/Multi-agent Interactions/Multimodality

Post Date
04/12/2024
Description

Advisor: Prof. Mohit Bansal (https://www.cs.unc.edu/~mbansal/)
Mentor: Elias Stengel-Eskin (https://esteng.github.io)
Group: Murge Lab UNC-CH (https://murgelab.cs.unc.edu/)
Duration (Flexible): Apr 15th – Sep 15th, 2024 (5 months), at least 20 hours per week commitment.
Role: Research Assistant (with RAship stipend)
Contact: Elias Stengel-Eskin (esteng@cs.unc.edu) with some basic information about yourself, your transcripts, your CV, and any discussion about any prior Machine Learning / Programming experience you have.

Requirements from Candidates (Good to Haves):
– Undergrads or Masters students from Computer Science, Mathematics/Statistics, or Linguistics/Cognitive Science background.
– Strong foundation in machine learning and deep learning techniques. Familiarity with model architectures like transformers, etc.
– Familiarity with deep learning programming frameworks like Pytorch and Huggingface libraries.
– Preferred: experience with code generation (semantic parsing, text-to-code) or vision-language tasks.
– Strong analytical abilities to ask the right questions to come up with a hypothesis and then design experiments to test it.
– Candidates who are possibly interested in a research career or grad school (Master/PhD) or Machine Learning jobs in the future.

Project Description:

Large language models allow people to interact with digital systems using natural language. However, language is inherently ambiguous and underspecified, and can lead to high uncertainty. This project will focus on enabling safe and robust interactions that allow people to make informed choices on when to trust model outputs. Core topics include: how to better model uncertainty, how to leverage multiple models to address ambiguity, how to intelligently obtain information to reduce uncertainty, and how to produce outputs that help users form their own uncertainty estimates. Topics may also include: multimodality and multimodal generation, vision-language tasks.

Research Areas:
– Multi-agent interactions and collaboration.
– Acquiring information for uncertainty reduction.
– Calibration and confidence estimation in LLM reasoning.
– Attributable and interpretable text generation.
– Multimodal reasoning and generation.

Some recent and representative papers in the direction:
1. Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models, ICLR 2024 (https://arxiv.org/abs/2310.05861)
2. ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs (https://arxiv.org/pdf/2309.13007)
3. MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models (https://arxiv.org/abs/2402.01620)
4. Soft Self-Consistency Improves Language Model Agents (https://arxiv.org/abs/2402.13212)
5. Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training (https://arxiv.org/abs/2403.02325)
6. Did You Mean…? Confidence-based Trade-offs in Semantic Parsing, EMNLP 2023 (https://arxiv.org/abs/2303.16857)
7. Zero and Few-shot Semantic Parsing with Ambiguous Inputs, ICLR 2024 (https://arxiv.org/abs/2306.00824)
8. ReGAL: Refactoring Programs to Discover Generalizable Abstractions (https://arxiv.org/abs/2401.16467)
9. GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations (https://arxiv.org/abs/2402.12348)
10. VideoDirectorGPT: Consistent Multi-scene Video Generation via LLM-Guided Planning (https://arxiv.org/abs/2309.15091)
11. Any-to-Any Generation via Composable Diffusion (https://arxiv.org/abs/2305.11846)

For inquiries or to express your interest, please send an email to esteng@cs.unc.edu

Faculty Advisor
Mohit Bansal
Research Supervisor
Elias Stengel-Eskin
Faculty Email:
Type of Position
Availability
Website
Post End Date
08/15/2024

Undergraduate Research Assistant Opportunities at (MURGE Lab) on Mixture of Expert, Model Merging, Efficient Models, and Continual Learning.

Post Date
04/10/2024
Description

Advisor: Prof. Mohit Bansal (https://www.cs.unc.edu/~mbansal/)
Mentor: Prateek Yadav (https://prateeky2806.github.io/)
Group: Murge Lab UNC-CH (https://murgelab.cs.unc.edu/)
Duration (Flexible): Apr 15th – Sep 15th, 2024 (5 months), at least 20 hours per week commitment.
Role: Research Assistant (with RAship stipend)
Contact: Prateek Yadav (praty@cs.unc.edu) with some basic information about yourself, your transcripts, your CV, and any discussion about any prior Machine Learning / Programming experience you have.

Requirements from Candidates (Good to Haves):
– Undergrads or Masters students from Computer Science, Mathematics or Statistics background.
– Strong foundation in machine learning and deep learning techniques. Familiarity with Model architectures like transformers, etc.
– Familiarity with deep learning programming frameworks like Pytorch and Huggingface libraries.
– Strong analytical abilities to ask the right questions to come up with a hypothesis and then design experiments to test it.
– Candidates who are possibly interested in a research career or grad school (Master/PhD) or Machine Learning jobs in the future.

Project Description:
Machine Learning has been evolving very rapidly and often people specialize models like LLama, and LLava to their specific applications to create domain-specialized models. The projects would revolve around the goal of recycling these existing models to create better modular models that are capable of solving unseen tasks and generalizing them to new datasets/domains in a zero/few-shot manner. Moreover, these models need to be continually adapted to new domains. The techniques involved would be around ideas related to Parameter Efficient finetuning, a Mixture of Expert models, Model Merging, and Composition.
Research Areas:
1. Enabling decentralized collaborative development of models, including modular architectures, cheaply-communicable updates, and merging methods
2. Developing generalist models by creating Mixture of Expert Models system.
3. Continual Model Adaptation and Learning
4. Parameter and Compute efficiency.

Some recent and representative papers in the direction;

1. TIES-Merging: Resolving Interference When Merging Models, NeurIPS’23 (https://arxiv.org/abs/2306.01708)
2. Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy, ICLR’24 (https://arxiv.org/abs/2310.01334)
3. ComPEFT: Compression for Communicating Parameter Efficient Updates via Sparsification and Quantization (https://arxiv.org/abs/2311.13171)
4. Loramoe: Revolutionizing Mixture of Experts for Maintaining World Knowledge In Language Model Alignment (https://arxiv.org/pdf2312.09979.pdf)
5. Modular Deep Learning (https://arxiv.org/abs/2302.11529)
6. Learning to Route Among Specialized Experts for Zero-Shot Generalization (https://arxiv.org/abs/2402.05859)
7. Model Stock: All we need is just a few fine-tuned models (https://arxiv.org/abs/2403.19522)

For inquiries or to express your interest, please send me an email at praty@cs.unc.edu

Faculty Advisor
Mohit Bansal
Research Supervisor
Prateek Yadav
Faculty Email:
Type of Position
Availability
Post End Date
05/15/2024

Engage in Research Serving Survivors of Sexual Assault

Suggested Fields
Post Date
04/03/2024
Description

The Better Tomorrow Network is the world’s first research network dedicated to serving survivors of sexual assault. We’re rapidly expanding our research portfolio, and are delighted to grow our team of Undergraduate Research Interns, as well.

Research Interns will contribute 10 hours per week, with most tasks centering around the expansion of our new research registry for survivors of recent sexual assault. Examples of tasks that the selected candidate will receive training on and help us complete include: outreach to programs to explore their interest in participating in the research registry (including cold calls, emails, and virtual meetings), identifying fundraising opportunities for the Better Tomorrow Network, conducting literature searches, and organizing information within Microsoft Excel. Additional tasks may be available depending on the selected candidate’s interests.

This research intern position is an especially strong fit for students interested in pursuing careers in non-profit administration, clinical research, psychology, and public health. Prior research experience is appreciated, but not required. Strong verbal and written communication skills required. Students from all backgrounds and identities are encouraged to apply.

If interested in applying, please email the following to Dr. Andrea Massa at btn@unc.edu: (1) A cover letter describing why you would be an excellent fit for the team and (2) a copy of your resume or CV. Applications will be reviewed after the position’s close date. The selected applicant will begin their role in May or June (dependent on candidate’s availability).

Thank you, and we look forward to reviewing your application!

Faculty Advisor
Samuel McLean, MD, MPH
Research Supervisor
Andrea Massa, PhD
Faculty Email:
Type of Position
Availability
Website
Post End Date
04/17/2024

Undergraduate Research Assistant

Suggested Fields
Post Date
03/28/2024
Description

Undergraduate research assistants are wanted to assist in ongoing research being conducted by Dr. Cope Feurer within the Child and Adolescent Anxiety and Mood Disorders Program (CHAAMP) in the Department of Psychiatry. Dr. Feurer’s lab focuses on the relation between stressful contexts and depression risk in youth, with a particular focus on the affective mechanisms linking stress and depression. Specifically, this lab uses a multiple levels of analysis approach integrating fMRI, EEG, physiology, and behavior to examine which youth are most likely to become depressed in the context of stress exposure, who is most likely to generate stress in their lives, and how stress exposure impacts the development of neurobiological mechanisms of risk.

Undergraduate research assistant volunteers in the lab can be involved in a variety of tasks including, but not limited to, coding life stress events, assisting with laboratory visits, assisting with EEG set up and clean up, preprocessing EEG and physiological data, and data entry.

Research assistants are asked to commit to working in the lab for at least two consecutive semesters for a minimum of 6-8 hours a week.

If interested in applying for a volunteer research assistant position in the lab, please email cope_feurer@med.unc.edu for an application. Applications are due by Friday April 12, 2024 at 5pm.

Faculty Advisor
Cope Feurer, PhD
Research Supervisor
Cope Feurer, PhD
Faculty Email:
Type of Position
Availability
Post End Date
04/12/2024

Research Assistant for Disordered Eating and Obesity Research Lab

Suggested Fields
Post Date
03/18/2024
Description

The Living F.R.E.E. Lab is a research group focused on developing, implementing, and evaluating equitable interventions for people of color managing disordered eating and chronic disease. We conduct mixed-methods research (e.g., quantitative and qualitative) to elucidate the mechanisms influencing the eating behaviors of underserved populations, and pilot interventions to treat binge eating and obesity. We are looking for undergraduate and graduate research assistants to assist with all aspects of the research project, including but not limited to conducting qualitative interviews, assisting with data collection and entry, data analysis, conducting literature reviews, engage in journal reviews, and dissemination of results through publications and presentations. This position will be virtual and research assistants are required to work for 5-10 hours per week and attend 2 weekly lab meetings. Applicants can receive course credit (PSYC/NSCI 395) or volunteer.

Please complete the attached application: https://docs.google.com/document/d/1-7jqPa0qftJdzmu-_S0TtSpqcvPQx0sn/edit?usp=sharing&ouid=115435165785152936235&rtpof=true&sd=true and email it to: julianr6@live.unc.edu.

All applications must be received by Monday, April 15, 2024, for priority consideration for the FALL 2024 semester.

Faculty Advisor
Rachel Goode
Research Supervisor
Rachel Goode
Faculty Email:
Type of Position
Availability
Website
Post End Date
04/16/2024

Undergraduate Research Assistant (RA)

Suggested Fields
Post Date
01/25/2024
Description

Opening available for an Undergraduate Research Assistant to assist in the daily research activities in the Biobehavioral Research on Addiction and Emotion (BRANE) Laboratory in the Department of Psychology and Neuroscience under the direction of Dr. Stacey Daughters. In the BRANE lab we aim to develop a better understanding of the behavioral and neural mechanisms that contribute to substance use disorder and to generate novel treatments that target these mechanisms. In this context we utilize tools such as behavior modification, electroencephalography (EEG), and noninvasive brain stimulation (NIBS). Minimum commitment of 1 semester and summer, 8-10 hours a week. Volunteer or course credit during the school year. The possibility of hourly pay during the summer. This is an excellent position for applicants interested in gaining valuable research and clinical experience prior to applying to graduate school. Interested applicants should send a resume/cv and the application (Application Can be found on the website under “GET INVOVLED”). Additional information about the lab can be found on the website: branelab.web. unc.edu. Applications will be reviewed on a rolling basis.

Faculty Advisor
Dr. Stacey Daughters
Research Supervisor
Melissa Puopolo
Faculty Email:
Type of Position
Availability
Website
Post End Date
04/25/2024

Undergraduate Research Assistant

Post Date
01/25/2024
Description

Dr. Can Chen, a faculty member of the School of Data Science and Society, is seeking undergraduate research assistants to join his research team in the field of data science. The research focuses on developing and applying artificial intelligence and dynamical systems techniques to solve problems in the field of data science. Dr. Chen’s research interests span a diverse range of fields, including control theory, network science, tensor algebra, numerical analysis, data science, machine learning, deep learning, hypergraph learning, data analysis, and computational biology.

Faculty Advisor
Can Chen
Research Supervisor
Can Chen
Faculty Email:
Type of Position
Availability
Post End Date
06/30/2024

Hydrodynamic Quantum Analogs with Walking Droplets

Post Date
01/05/2024
Description

The Physical Mathematics Lab (PML) (Intro Video) offers a wide range of interdisciplinary problems that find motivation in very diverse fields, including soft matter, fluid mechanics, biophysics and quantum mechanics. One of PML’s themes is the study of new Hydrodynamic Quantum Analogs (HQAs) with walking drops (Video). Millimetric liquid drops can walk across the surface of a vibrating fluid bath, self-propelled through a resonant interaction with their own guiding or ‘pilot’ wave fields. These walking drops exhibit features previously thought to be exclusive to the quantum realm. This system has attracted a great deal of attention as it constitutes the first known and directly observable pilot-wave system of the form proposed by de Broglie in 1926 as a rational, realist alternative to the Copenhagen Interpretation (Video & Read). At PML, we work to unveil and rationalize new HQAs, thus challenging the limits between the quantum & classical worlds. Our investigations blend experiments & mathematical modeling (theory & simulations), we can thus tailor your project according to your interests & skills. Prior research experience is not necessary, you just need to be eager to learn!

Faculty Advisor
Pedro Saenz
Research Supervisor
Pedro Saenz
Faculty Email:
Type of Position
Availability
Website
Post End Date
06/30/2024

Examining Mechanisms Underlying Performance Fatigability in Women

Suggested Fields
Post Date
11/14/2023
Description

The Motion Science Institute is currently recruiting healthy women between the ages of 18-30 to participate in a research study examining performance fatigability in women. Participants must have a BMI of ≥ 30 kg/m2 and not currently be on hormonal contraception.

Participants will receive free body composition analysis and $50 for taking part in this study.

If you are interested, please visit our website or contact Amber Schmitz by email at amberns@unc.edu.

Faculty Advisor
Dr. Eric Ryan
Research Supervisor
Amber Schmitz
Faculty Email:
Type of Position
Availability
Website
Post End Date
05/09/2024