Assistant Professor

Multiple Tenure Track Faculty Positions

● Schools and Colleges:
College of Science, Engineering and Artificial Intelligence

● Campus:
Brooklyn and Post Campuses, New York

The College of Science, Engineering, and AI (CSEA) at Long Island University is where the

frontiers of knowledge converge-uniting disciplines to solve the world's most complex

challenges, generate bold new scientific theories, and create models that transform discovery

into societal impact. At CSEA, ideas don't just advance science-they redefine what is possible.

CSEA is positioning itself as a national leader in convergence science, STEM education, and

interdisciplinary research. It will consolidate LIU's strengths in computing, engineering, life

sciences, mathematics, and health sciences, while advancing the One LIU Vision and the

University's strategic research priorities. By dissolving traditional disciplinary boundaries, the

College empowers students and faculty to approach complex problems with bold, integrated

solutions. Here, discoveries evolve into theories, models, and innovations that scale knowledge

and impact communities worldwide.

Education and Experience

* Ph.D. in Computational Biology, Bioinformatics, Biotechnology, Biomedical Engineering,

Pharmaceutical Sciences, or related fields

* Biomedical undergraduate background

* Understanding of fluid dynamics, pathology, biochemistry, anatomy (preferred, though not

requisite, in Gastrointestinal + Hepatobioliary anatomy, brain/heart/liver anatomies)

* Finite Element (FE) and Computational Fluid Dynamics (CFD) simulations for engineering

and biomedical applications.

* Multiscale modelling of human systems (Virtual Twins), with direct applications in precision

medicine and digital health.

* Material modelling using cutting-edge simulation frameworks.

* In-silico simulation of organ-level systems, including translational research in digital twins.

* Demonstrated expertise in computational drug discovery including molecular modeling,

library preparation model, toxicity prediction, QSAR, pharmacophore analysis, and

molecular dynamics simulations using platforms such as Dassault Systèmes

3

DEXPERIENCE and Discovery Studio BIOVIA. Expectations

* Available to work with hospital and clinician team for minimum 10 hours per week (20 h/

week is ideal).

* Able to assist the clinician team with, but not limited to, addressing inquiries related to data

management and engineering development software used for building the virtual twin

models. The initial project will be focused on the Liver and Gastrointestinal physiology.

* Oversight of the de-identified database: help ensure the proper formats/labels are used,

with clinician input.

* Collaborate with multidisciplinary teams at national and international hospitals

(Investigators, Research staff, IT teams, LHS).

* Understanding of computer databases, spreadsheets, and word-processing software.

* Demonstrated understanding of digital clinical data format and processing (dicom, stl, jpg,

png, tif, csv, xlxs.).

* Establishing a competitive research program in his/her area(s) of expertise.

* Develop and/or continue multidisciplinary collaboration.

* Satisfy teaching and service requirements consistent with the department policies.

The successful candidate will:

* Build and lead an independent research program in computational engineering and

digital health, with strong potential for competitive funding.

* Advance LIU's leadership in AI-driven multiscale simulation and Virtual Twin

technologies for pharmaceuticals, bioengineering, and precision medicine.

* Design and validate multiscale frameworks for translational biomedical research and

drug toxicity and efficacy - from atomistic to organ-level digital twins.

* Play a central role in curriculum development and teaching excellence for the new

Bachelor of Engineering in Digital Engineering, incorporating advanced simulation

methodologies into core courses.

* Mentor and inspire students, preparing the next generation of engineers and

computational scientists to address real-world biomedical and technological challenges.

* Collaborate across disciplines-including engineering, life sciences, and computational

biology-to develop…