- We are recruiting for PhD researchers for Fall 2027 starts. Please apply through the Dartmouth CS PhD program at the Guarini School of Graduate and Advanced Studies by December 15th.
- All hiring will center on the lab’s core focus of algorithm-hardware-software co-design for robotics. We are particularly excited about expanding our work with emerging compute platforms (e.g., FPGAs, ASICs, neuromorphic, and analog computing), alongside our ongoing work on GPUs, embedded systems, numerical optimization, and robot learning.
- In your application, please select Robotics as an area of interest and list Brian Plancher as one of the faculty members you are particularly interested in working with.
- Dartmouth offers application fee waivers to applicants meeting a number of eligibility criteria.
- Due to the volume of prospective-student inquiries, we are generally unable to meet individually with applicants or pre-review application materials before submission. We will carefully review applications during the admissions process and reach out directly to candidates whose interests and background appear to be a particularly strong match.
- Dartmouth Undergraduates and Master’s Student research opportunities vary substantially by term, project needs, mentoring capacity, and available funding. The best way to prepare to work with the lab is generally to take Parallel Optimization for Robotics which will next be taught in the Winter 2027 Term. See the information below for additional details.
- We do not currently have any open postdoctoral positions. Any future funded postdoctoral openings will be posted on this page.
- We generally do not take on remote or external research assistants who have not previously worked with the lab in person. Robotics research frequently requires time in the lab to deploy and evaluate systems on physical robots, and our experience has been that research collaborations are substantially more successful after first working together in person. In exceptional cases, we may consider remote work with someone who has previously worked with the lab in person, typically through a course or term-time independent study. If you are not currently at Dartmouth and have not previously worked with the lab, you should generally not expect us to be able to offer a research project.
We welcome students with a wide range of technical backgrounds who are interested in our research. Our work is highly interdisciplinary, spanning robotics, computer architecture, embedded systems, numerical optimization, and machine learning, with a unifying focus on developing edge computational systems through algorithm-hardware-software co-design.
Because of this breadth, we do not expect incoming researchers to already have experience in every area. Prior experience in robotics, parallel programming, or machine learning is helpful but not required. Instead, we particularly value strong foundations in applied mathematics, computer systems, and software engineering, as these skills translate across many of our projects and provide a strong foundation for learning project-specific material.
Most importantly, we value intellectual curiosity, commitment, clear communication, creativity, and the courage to learn something new. Students who enjoy bridging multiple technical domains and engaging deeply in collaborative research are likely to be a good fit. We also care about real-world impact and encourage contributions to outreach and education.
For Dartmouth undergraduates, master’s students, and PhD researchers interested in the lab, we strongly recommend taking Parallel Optimization for Robotics. It provides preparation for much of our research, and its course project can be an excellent way to begin exploring related research directions.
Expectations for Part-Time Undergraduate and Master’s Researchers
To make sure that term-time research can be a meaningful experience, we generally expect undergraduate and master’s researchers to:
- Commit at least 10 hours per week to research, roughly comparable to the time commitment of a course. Independent-study research credit may be possible for Dartmouth students.
- Provide a written progress update each week and discuss it through a scheduled project meeting.
- Participate in person (where applicable) for project meetings, hardware deployments, etc.
Research capacity varies from term to term, and meeting these expectations does not by itself guarantee that we will have an appropriate project or available mentoring capacity.
FAQs:
What kinds of research is the lab doing right now? You can find descriptions of current research directions on our projects page and our recent work on our publications page.
Broadly, our research focuses on performance engineering for computational robotics at the edge through algorithm-hardware-software co-design. Current and future directions include accelerated numerical optimization and model predictive control on GPUs; efficient robotics algorithms for resource-constrained platforms such as microcontrollers; hybrid approaches combining learning, sampling, and optimization; and emerging robotics computing architectures including FPGAs, ASICs, and neuromorphic systems.
Because individual projects often require substantial depth in a particular technical area, there is no single set of prerequisites for every project.
Is research funded?
Funding for undergraduate and master’s research should not be assumed and is not guaranteed. Available funding varies substantially by project, student eligibility, grant support, and time of year. Research may also be possible through an independent study for academic credit.
Dartmouth students are strongly encouraged to explore established Dartmouth research programs and other College funding opportunities. From time to time, the lab may have grant-supported paid research positions, but availability is limited and varies from term to term. We cannot guarantee that joining the lab will lead to funding now or in the future.
Admitted PhD students have 5 years of guaranteed funding through the Dartmouth Computer Science PhD program rather than through the undergraduate/master’s research process described above.
Can I work with the lab remotely?
As a general rule, we do not support purely remote research assistants. Our research frequently involves physical robotic systems, and even projects that are primarily computational often eventually require in-lab deployment and evaluation. In addition, our experience has been that remote collaborations work much better after first establishing a successful in-person working relationship. We therefore typically require researchers to work with us in person first (e.g., through a course or term-time independent study) before we would consider a subsequent remote arrangement.
Should I email before applying to the PhD program? Can you review my application?
You are welcome to indicate your interest in the A²R Lab in your application, and you should list Brian Plancher among the faculty you are particularly interested in working with. However, due to the volume of prospective-student inquiries, Brian is generally unable to schedule meetings with prospective applicants or provide application pre-reviews before the admissions deadline. You do not need to contact the lab in advance to receive full consideration. Applications will be reviewed carefully as part of the normal Dartmouth CS admissions process, and we will reach out directly to candidates whose interests appear to be a strong match.
Where can I learn more or prepare for research in the lab?
For Dartmouth students, Parallel Optimization for Robotics is excellent preparation for many of our projects.
You can also check out Brian’s Autonomy talk on 5/20/25, PhD Dissertation Defense on 4/26/22, and talk at Barnard on 12/14/21, which provide overviews of several past research directions. For deeper background in robotics algorithms and mathematics, Russ Tedrake’s Underactuated Robotics is an excellent resource. For GPU programming, this online course provides a useful introduction.
Questions not addressed above? Email us at plancher+A2R@dartmouth.edu.