Research Opportunities for Students
Here you can find open research opportunities in the different faculties:
Please send your CV directly to the contact person Email.
Search by Professor's name or Faculty
job description:
Flow Physics Lab
The Flow Physics Lab focuses on theoretical and computational investigations of flow physics, with specific emphasis on instability and transition to turbulence. The common thread guiding our research is using a minimal number of elements to describe physical phenomena. Therefore, canonical settings, where effects of various parameters can be isolated, are often analyzed. Investigations of flow physics provide us with guidelines for controlling the flow and avoiding undesirable phenomena (e.g. stall, noise and vibrations), leading to safer, quieter and more efficient aerial vehicles with lower drag and fuel consumption.
Department Web Site:
job description:
Coordination and Control of Multi-agent systems
The Cooperative Networks and Controls Lab (ConNeCt) is seeking motivated students to pursue a MSc, PhD, or Post-Doc.
We are looking for students with a broad interest in multi-agent and network control theory. Problems we consider include, but are not limited to control and coordination of multi-robot systems design of robust and resilient network systems networked event-triggered control problems graph-theoretic methods for control of multi-agent systems.
The successful candidate should have a strong background in control theory and mathematics.
Interested candidates are invited to send a copy of their CV, academic transcript, and brief research statement to Prof. Daniel Zelazo
Department Web Site:
job description:
PhD or Postdoctoral Position
About:
Join the Water Systems Lab to ensure safe reclaimed-water use for intermittent irrigation under dynamic conditions. Work spans microbial/AMR dynamics, disinfection & biofilm control, and a digital twin for system optimization using EPyT-C/EPANET.
Start: Jan 1, 2026
Support: BMBF–MOST
Responsibilities:
• Model multi-species transport, regrowth, and ARB/ARG fate.
• Fuse pilot data into probabilistic/ML frameworks; design control & disinfection strategies.
• Collaborate with TUM partners; publish results.
Qualifications:
• PhD track: M.Sc. in a relevant field.
• Postdoc: PhD in Environmental/Civil/Chemical Engineering (or related).
• Strength in water-quality modeling/microbiology/AI; Python tools (EPyT-C, WNTR, EPANET-MSX) a plus.
• Strong writing and teamwork.
Apply:
Send CV + cover letter to ostfeld@tx.technion.ac.il
Department Web Site:
job description:
Privacy-preserving data and experimentation platform
Many important mobility questions cannot be answered without careful data collection and controlled experiments. This project develops a privacy-preserving platform that supports safe mobility data collection and, later, controlled studies of information interventions.
What you might work on:
Depending on your interests, you may contribute to privacy-aware data pipelines, measurement validation, and utility benchmarking (how privacy choices affect what analyses remain possible). The platform is modular, so contributions can be well-scoped and meaningful even without building a full-stack app.
Why it matters:
Infrastructure that is privacy-respecting and scientifically validated can enable research that is both ethically sound and practically useful. It also creates opportunities for collaboration with cities and mobility organizations.
Who we are looking for:
Students interested in applied research engineering at the boundary of mobility, data, and privacy. Comfortable coding helps, but the key is careful thinking, testing, and documentation—making tools that others can trust.
Department Web Site:
job description:
Designing information in mobility systems
When many travelers respond to the same guidance (e.g., route recommendations), the outcome is not simply everyone gets faster routes. Their responses interact through congestion in mobility systems. This project develops system-level models and algorithms for information design: how agencies or platforms should structure guidance so that the whole network performs better, not just individual trips.
What you might work on:
You will build equilibrium models that represent different levels of responsiveness to guidance. You will then study how changing the information policy (e.g., what is communicated, how precise it is, and how it is delivered) affects congestion outcomes, reliability, and distributional impacts across travelers. The project gradually moves toward designing information policies using optimization methods and testing them on real-size networks.
Why it matters:
As platforms become central in shaping mobility, we need scientific tools to prevent guidance from causing harmful feedback loops, instability, or inequities—and to design guidance that is robust and beneficial.
Who we are looking for:
Students who like systems thinking, networks, game theory, and optimization. A good fit if you enjoy turning a complex socio-technical problem into a clear model, and then building algorithms and numerical experiments to answer it
Department Web Site:
job description:
Privacy-preserving mobility choice modeling
Mobility data is powerful, but it is also sensitive. Cities and platforms increasingly want to learn how people travel while protecting privacy. This project will develop methods that allow us to obtain meaningful behavioral insights via structural spatiotemporal choice models, meanwhile ensuring privacies are protected.
What you might work on:
You will build and test models that connect mobility decisions (such as route, mode, or timing) to context (time, network conditions, and constraints). You will then study how privacy-preserving data changes what can be reliably learned, and develop estimation strategies that remain stable and interpretable under these constraints.
Why it matters:
Mobility data was historically difficult to collect at scale, and privacy has been one of the main barriers. By developing privacy-preserving modeling and inference methods, we aim to extract meaningful behavioral insights that can inform better services and policies while protecting individuals. This helps move transportation planning into a true large-data regime, where more advanced and data-hungry methods can be applied responsibly.
Who we are looking for:
Students who enjoy a mix of modeling and data work. A strong fit if you like statistics, discrete choice, optimization, and reproducible computational experiments. Programming experience (Python/R) is helpful, but motivation to learn matters most.
Department Web Site:
https://moca-technion.github.io
job description:
Description of the laboratory:
Research on Biology of Facial expressions
Proposed position:
MS.c, Ph.D, Post-doctoral Researchers
Department Web Site: