Biophysics Jobs in Delaware (NOW HIRING)

Context of Job:
We invite applications for multiple Postdoctoral Fellow positions as part of the NSF AI Institute for Human-AI Cooperation (NSF HAIC) (https://haicinstitute.org/), a $21.5 million multi-institutional research initiative across the University of Delaware (UD), Delaware State University, University of Florida, University of Pennsylvania (Penn), Princeton University, and other partners, supported by a 5-year (2026-2031) grant from the U.S. National Science Foundation. Postdoctoral Fellows will actively collaborate across sites and may have joint appointments at UD, Penn, and other partner institutions.
NSF HAIC's mission is to develop trustworthy collaborative AI that strengthens human expertise, shapes the future of rehabilitation and health care, and cultivates the next generation of leaders in AI. Postdoctoral Fellows will conduct foundational and/or applied AI research, contributing to one or more of the three thematic working groups: 1) Integration and extraction of actionable information from multimodal spatiotemporal data, including physiological signals, imagery, and natural language, via adaptive machine learning; 2) Personalization of sequential decision making, biophysical digital twins, and the operation of digital and physical systems; and 3) Human-AI symbiosis, with the initial use case of aiding in neuromuscular rehabilitation. Researchers will collaborate with leading scholars in AI, machine learning, robotics, reinforcement learning, and data science, accessing state-of-the-art facilities and equipment (instrumented biomechanical labs with wearable robotics, exoskeletons and prostheses, leveraging extensive datasets, and harnessing high-performance computing (HPC) systems with dedicated research software engineers and access to NVIDIA systems engineers) that can be leveraged depending on their role. Postdoctoral Fellows will become key contributors and will be provided with comprehensive training to enhance their scientific, technical, and professional skills and experiences through a structured mentoring plan.
While the primary location and co-mentors will be at UD, Postdoctoral Fellows may receive joint appointments with other NSF HAIC sites, including the University of Florida, University of Pennsylvania, or Princeton University. Jointly appointed postdocs will have co-mentors and access to facilities and resources at both institutions, along with travel funding for collaborative visits to achieve project objectives. In particular, joint UD-Penn NSF HAIC postdocs may be housed in the General Robotics, Automation, Sensing, and Perception (GRASP) Lab (https://grasp.upenn.edu) and the Wharton School (https://wharton.upenn.edu/) at Penn, and may collaborate with researchers in the Perelman School of Medicine.
These positions are supported by the U.S. National Science Foundation under Cooperative Agreement No. 2433450. Continuation of each position is contingent upon performance and continued availability of these funds. Notice of non-renewal is not required.
Major Responsibilities:

• Participate as an integral member of a highly technical, mission-driven research team, forging active communication among HAIC researchers across multidisciplinary working groups and mentoring junior members when necessary.
• Assist the team in relevant aspects of data collection, dataset preparation, software implementation, model validation, laboratory maintenance, cyberinfrastructure development, and documentation, depending on the role.
• Conduct computational and/or human subjects research experiments within the project's scope.
• Actively participate in manuscript preparation and revision for scientific publications aimed at top-tier conferences and journals.
• Regularly prepare or contribute to research presentations.
• Assist in technical report writing through documentation of research goals, milestones, issues, challenges, outcomes, and future plans.
• Efficiently use and aid in the selection of additional equipment and computing resources as needed.
• Assist and advise both novice and experienced researchers in developing and completing seed projects.
• Contribute to forging new collaborations, grant writing, or translation of intellectual property.
• Prepare workshops, tutorials, dataset challenges, outreach materials, and education and workforce development resources relevant to research.
• Participate in other project deliverables and dissemination efforts.
• Perform other related duties as assigned.
  

Qualifications:

• A Ph.D. degree (by early 2027) in computer science, data science, statistics, bioinformatics, electrical engineering, computer engineering, biomedical engineering, mechanical engineering, operations research, robotics, computational neuroscience, applied mathematics, physics, or a closely related discipline.
• Excellent oral and written communication skills both generally and specifically with regard to complex technical and scientific topics.
• Strong problem-solving and reasoning skills.
• Ability to work effectively with researchers and team members at varying experience levels.
• Ability to organize a research project, maintaining focus on goals, tracking details, efficiently communicating on issues and outcomes, and executing independently and in collaboration.
• Aptitude for gaining new skills and learning quickly in a dynamic technological environment.
  

Desired Qualifications (at least one):

• Primary authorship in top-tier AI, machine learning, data science, computer vision, natural language processing, knowledge representation, statistics, applied mathematics, or robotics conference or journal papers.
• Documented experience in the analysis of neuroscientific or biomedical data or signals (e.g., motion capture, force plates, EMG, or physiological signals).
• Experience with conducting human-subjects experiments, especially involving neuroscience, biomechanics, human-computer interaction, or brain-machine interfaces.
• Experience with dynamic modeling, including rigid body dynamics, biomechanics, and neural mechanisms, and applying data-driven methods to learn or identify models from empirical data.