Seeking highly motivated students to start Summer Session II, with the possibility to continue during Fall 2024. Dr. Sheeran’s laboratory is exploring the comparative prevalence of observational and experimental studies in health psychology journals.

Explicitly, we seek to answer the following questions: Are observational studies more likely to be (a) conducted and (b) cited than intervention studies in health psychology? Is there a self-fulfilling prophecy?
Undergraduate Research Assistants involved in this study will have the chance to contribute to different aspects of the research, such as searching for articles within the SCOPUS database and inputting/organizing data in Excel files. Students in this position should be available for a minimum of 8 hours/week. If you’re considering graduate school, this is a great opportunity to experience working on a research study. We strongly encourage applications from diverse backgrounds. If interested, please email your resume to Yifei Pei (yifeipei@email.unc.edu).

The Frohlich lab is seeking highly motivated undergraduate students to join our team as Research Assistants (RAs). Our lab is dedicated to understanding the neural basis of behaviour and developing novel treatments for psychiatric and neurological disorders through the interdisciplinary study of network dynamics. We integrate preclinical and clinical research using electrophysiology, imaging, brain stimulation, cognitive assays, and clinical trials methodology. RAs will assist in various research activities within the cognitive/pre-clinical arm under Dr. Agnieszka Zuberer or the clinical arm under Dr. Flavio Frohlich. Our work is supported by advanced data analysis, computational modelling, and machine learning, all in close conjunction with our experimental efforts.

We expect our RAs to commit to 60 hours per month. Candidates should have a minimum GPA of 3.5. We also value candidates who have participated in extra-curricular activities. As an RA, you will be involved in data collection, analysis, and interpretation, aiding in the preparation of research presentations and publications, participating in lab meetings, and contributing to ongoing projects. The Frohlich lab fosters a learn-as-you-go approach, encouraging RAs to develop their scientific skills throughout their tenure.

Responsibilities include participating in data collection, analysis, and interpretation, supporting the preparation of research presentations and publications, engaging in lab meetings, and contributing to ongoing projects. The environment at the Frohlich lab supports a learn-as you go approach, and candidates are encouraged to develop their scientific skills throughout their tenure as an RA. We have a track record of RAs being accepted into either medical school or graduate school after working with us. We attribute this to the high level of mentorship support being offered to RAs from the rest of the research team.

Ideal candidates will have a strong academic record, a keen interest in cognitive neuroscience and psychiatry/psychology, particularly in the areas of major depressive disorder and anxiety disorders, and strong organizational and communication skills. This position offers the opportunity to gain hands-on research experience in a cutting-edge lab, work closely with a team of experienced researchers, develop skills in data collection, analysis, and scientific writing, and enhance academic and professional development in the field of neuroscience and psychiatry.

Interested candidates are invited to submit a current resume/CV, a cover letter detailing their interest in the position and any relevant experience, and an unofficial transcript. Please send your application to Max Archibald Montgomery at montgom@email.unc.edu. We are excited for you to join us in making a difference in the field of psychiatric research!

The cognitive arm of the Frohlich lab, led by Dr. Agnieszka Zuberer, is actively seeking highly motivated undergraduate students to join our cognitive research team as Research Assistants (RAs). Our lab specializes in the study of emotion through the use of emotionally evocative movie clips, employing pupillometry and subjective annotations as outcome measures. We are committed to understanding the neural basis of behaviour and advancing treatments for neurocognitive disorders by exploring network dynamics. Our cognitive arm utilizes advanced tools such as EEG, fMRI, and TACS to delve into brain function, supported by sophisticated data analysis, computational modelling, and machine learning techniques.

We expect our RAs to commit to 60 hours per month. Candidates should have a minimum GPA of 3.5. We also value candidates who have participated in extra-curricular activities. As an RA, you will be involved in data collection, analysis, and interpretation, aiding in the preparation of research presentations and publications, participating in lab meetings, and contributing to ongoing projects. The Frohlich lab fosters a learn-as-you-go approach, encouraging RAs to develop their scientific skills throughout their tenure.

Ideal candidates will possess a strong academic record, a keen interest in cognitive neuroscience and psychiatry/psychology, particularly in areas related to major depressive disorder and anxiety disorders and exhibit strong organizational and communication skills. This position offers a unique opportunity to gain valuable research experience, work alongside experienced researchers and clinicians, and enhance your academic and professional development in neuroscience and psychiatry.

To apply, please submit a current resume/CV, a cover letter detailing your interest in the cognitive research arm, and an unofficial transcript to Max Archibald Montgomery at montgom@email.unc.edu. We look forward to reading your application!

[Research description==Qualifications]
– Goal: Document & refactor code for simple & flexible ANN experiments (Engineering & Science)
– Time commitment: 6+ hours/week
– Date & duration: Starting August 2024 for 1+ years
– Language: python (pytorch, numpy, pandas, sklearn, etc)
– Work style: Transparent & Concise
– Course of research:
1. Code library literature review
2. Code reading + documentation (Graphic & written)
3. Refactor code bases

[You get]
– Amazing end-to-end technical & conceptual skills for Science & Machine learning
– Practical technical skills: python virtual environment, adjusting computing platforms, IDE, git, python libraries (pytorch, numpy, pandas, sklearn, etc), drawing figures, presentation skills
– Conceptual skills: concise coding, computer architecture, ANN, machine learning research pipeline, computational neuroscience
– Supervisor acknowledged as the best coder throughout past labs
– Recommendation letter
– (Long horizon, 1+ year) Paper publication

[To apply]
Please fill out the following google form: https://docs.google.com/forms/d/e/1FAIpQLSfdKZyD2KxAAyXczqZuSrC03Gied9qHFt-NxCUTDiRR1zTbgA/viewform?usp=sf_link
We’ll keep this form open during summer and reach out for interviews late July-Early August.

[More information]
Hantman Lab: https://www.med.unc.edu/neuroscience/hantmanlab/
Jaesung Yoo: https://jaesung.tech.blog/

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

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.

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!

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.