Graduate student and postdoc positions

We always look for excellent graduate students or postdocs to join the lab, (including interns from the Dkalim, Ashalim, and Eitan programs). If any of the vision sciences catches your imagination and research is your passion, please do contact us to tell us about yourself and learn more about the lab and its research.  Research positions for the 2020/2021 academic year are described in this page.  Unless stated differently, most projects offer opportunities at all of the M.Sc., Ph.D., and Postdoc levels.

If you are looking for a senior project during your BA/BSc, please refer to the Senior Projects page.

Algorithmic puzzle solving for computational archeology

Automatic solving of jigsaw puzzles has been receiving increasing interest in the computational vision community, and our lab has been pushing the abilities of such algorithms to new fronts. Following this initial success (and a follow up senior project on robotic puzzle solving) we will set to study important (and yet unexplored) extensions of the problem, as well as important applications, in particular in computational cultural heritage (and more specifically in computational archeology). Strong background in computer science, algorithms, and computational geometry are a plus. For details please contact: Prof. Ohad Ben-Shahar

Computational eye care

Computational healthcare is exploding and related to vision is of course eye care. Both big data and algorithmic tools provide oppostunities like never before. In collaboration with clinicians and scientists from Soroka’s Eye Department, we set to study and develop novel methods for diagnosis and treatment of various eye and vision conditions to aid eye care professionals. Research likely to involve both basic and applied science and significant parts are related to the eye movements exploration topic below. For details please contact: Prof. Ohad Ben-Shahar

Computational visual astrophysics

Astrophysics, now more than ever, is a scientific fields relying heavily on the processing of visual or visual-like infromation coming in vast volumes from various sensors on earth and in space.  In collaboration with data scientists and astrophysicists from the NJIT and NASA, we set to study  aspects of the solar system (in particular, space weather) using computaitonal vision and machine learning tools and various research projects are available taht related to finding patterns in solca and space data, forecasting solar eruptions and otehr space weather events. For details please contact: Prof. Ohad Ben-Shahar

Computational visual contour completion

Visual completion is a fundamental perception capacity of human vision and a crucial function for computer vision.   Previous computational research in the iCVL has explored the visual contour completion problem in a biologically-inspired and perceptual-consistent fashion, pushing a unique approach that considers the problem not in the image plane but rather in a mathematical space that abstracts the primary visual cortex.  Candidate interested in this problem will study both computational aspects and perceptual issues. No special background is needed, but previous experience with tools from analysis, differential equations, differential geometry and numerical analysis, as well as background in visual perception are a plus.  For details please contact: Prof. Ohad Ben-Shahar

Behavioral exploration of Contour Completion

Related to the computational exploration of contour completion, the topic raises interesting behavioral questions. Two of these are of particular interest for the lab:

  • The role of the occluder on amodal completion: Previous perceptual research in the iCVL has explored the effect of various visual cues on the grouping of the inducers, and in this research project we would like to explore the role of the occluder on shape of the computed contour. In particular, we are interested to understand if the shading pattern on the occluder has a role in the perceptual completion outcome.
  • Shape completion in neglect patients: Visual completion is a fundamental perceptual capacity taht allows human observers experience whole and coherent objects from visual fragments. Does this capacity depends on the integrity of spatial vision? In this project we will explore this question by  testing neglect patients who suffer from a particular spatial deficit related to their spatial visual field. Key in this project is the formulation of special stimuli in which normal and neglect patients are predicted to make different completions .

Strong background in Cognitive and Brain sciences is a plus but not a must.  For details please contact: Prof. Ohad Ben-Shahar

Hyperspectral vision

While color vision allows humans and other primates to see the world in amazing detail, light in the visible spectrum carries more information than just “red, green and blue”, advanced “Hyperspectral” cameras allow us to explore the would around us in greater detail revealing the hidden colors that lie beyond the capabilities of human vision. Following initial research in the lab that results with new technology for hyperspectral acquisition and reconstruction, several research question emerge about the links between hyperspectral images,  color images, and vision.  For details please contact:  Prof. Ohad Ben-Shahar.

Eye-movements exploration

Humans execute rapid eye movements between locations well over 100,000 times a day. These movements are an integral part of our visual system, serving in acquiring relevant information, tracking visual targets, and allowing us to operate in our world. Tracking eye-movements and measuring how they change in response to different visual stimuli provide a window into human visual perception. Using either of the ICVL’s head mounted, remote, and portable eye trackers, we are able to explore various aspects of these eye movements and utilize them for the understanding of human behavior for solving specific computational (and occasionally applied) computational tasks in order to incorporate this knowledge into artificial visual systems. For details please contact: Prof. Ohad Ben-Shahar

Computational vision for the study of fish behavior

Do you care for both computational tools and biological issues? In a long term project with Prof. Ronen Segev and Prof. Moshe Kiflawi from the Life Sciences department we study both behavior and neurophysiology of the archer fish with some visionary applications to futuristic robotics. As part of this project we intend to study and develop fully automatic tools and algorithms for the analysis of the fish behavior in the wild (in this case, the reef in Eilat), set a fully operational experimental system, and explore the latter in various experiments. For details and how to apply please contact: Prof. Ohad Ben-Shahar.

Scene and object recognition

The ability of humans to recognize and classify both visual scenes and objects is astonishingly rapid and reliable, even when the number of examples experienced from a particular class is  very small. Part of this capacity may be assisted by a hierarchical representation of visual classes, reminiscent of artificial datasets like ImageNet, and computational foundations represented as ontologies of the sort our lab has been studying for some time . While ImageNet is based on  hierarchy crafted from lexical relationships,  a question remains about the perceptual hierarchy that may be innate to the human visual system and how it may have formed during development. In this research project we will explore this question, first using psychophysical and behavioral tools, followed by a computational inquiry that could eventually lead to better recognition algorithms. For details please contact: Prof. Ohad Ben-Shahar

The phase problem in natural image statistics

The statistics of natural images is a source for great advancements in computer vision, as well as in computational modeling of biological and human vision. In this research project we will explore the implications of certain statistical properties of images for a problem closely related to the  Phase Problem in crystallography and investigate applications for image representation, compression, retrieval and search operations. For details please contact: Prof. Ohad Ben-Shahar

Deep learning for inverse problems in computer vision

Ill-posed inverse problems in vision (or other domains) are one-to-many problems where the I/O mapping is under-determined.  In some very basic sense, the vision problem as a whole is one grand inverse problem.  For decades now the computer vision community has been addressing such problems using regularized optimization – a fancy terms to say more constraints are needed to single our certain solutions over others. Could deep learning, the new king in town, do better? In this research project we will explore this topic in depth and tackle some of the most challenging vision problems.  For details please contact: Prof. Ohad Ben-Shahar