Students seeking a hands-on, leading-edge summer research experience are invited to apply to the Armour R&D Summer Research Immersion Program in the global city of Chicago. This unique, competitive program offers students from throughout the country the opportunity to receive engineering course credit for participating in one of a diverse range of engineering research projects with Illinois Tech faculty mentors across all engineering disciplines. Please note: As this is a for-credit opportunity, tuition will be charged.
Start Date: May 23rd
End Date: July 15th
Application Deadline: May 2nd
For Undergraduate Students
Instructor: Jafar Saniie
Eligibility: Graduate Students
Instruction Method: Hybrid
Students in this course join the research team at the Embedded Computing and Signal processing Research Laboratory (http://ecasp.ece.iit.edu/), where they will have the opportunity to design and learn advanced topics in Internet of Things, Embedded Computing, Artificial Intelligence, and Deep Learning. This course focuses on practical applications of computer vision, deep learning, and wireless communications using smart phone, and embedded computing such as NVIDIA Jetson Nano, Raspberry Pi, and Xilinx PYNQ development boards.
Instructor: Francisco Ruiz
Eligibility: Graduate Students
Instruction Method: In-person
Several team projects:
-
- Adaptive Cycle Engnes, where compressions and expansions do not follow a fixed sequence, with air storage and energy recovery.
-
- Levitating land vehicles, designing and building single-seat prototype with induction-based magnetic levitation.
-
- EcoCar support, converting a 2023 Cadillac Lyriq electric vehicle to four-wheel drive.
Instructor: Scott Dawson
Eligibility: Graduate Students
Instruction Method: Hybrid
This project will study the behavior of unsteady aerodynamic systems, such as wings and fins that can pitch and flap, and that may also encounter strong gusts and currents. To understand and model these systems, we will combine aspects of classical theory with numerical simulations and experimental data.
Instructor: Scott Dawson
Eligibility: Graduate Students
Instruction Method: Hybrid
This project will focus on studying the properties of turbulence in fluid flows. In particular, students will implement algorithms to characterize and understand statistical structures in canonical turbulent flows. Students will learn to run computational fluid dynamics codes, and apply various data analysis methods. A background and interest in fluid mechanics, mathematics and programming is desirable.
Instructor: Abhinav Bhushan
Eligibility: Graduate Students
Instruction Method: Online
The goal of this project oriented class will be to learn about synthetic biology principles for applications in food and health. Students will undertake several small projects before taking on a larger project. Through the coursework, the students will learn about cutting-edge aspects such as CRISPR. Students will also be exposed to emerging trends in synthetic biology.
Instructor: Abhinav Bhushan
Eligibility: Graduate Students
Instruction Method: Online
Wearable devices have the potential of transforming health. Do you know how wearable devices work? The goal of this class will be to learn about technologies that could be used in wearable device and then develop a wearable device through a project. Some of the projects could move to prototyping. Healthcare remains the fastest growing sector in the US with a large number of engineers bring hired by medical device companies. Students will also be exposed to emerging trends in the larger medical device industry especially wearable and point of care devices.
Instructor: Keigo Kawaji
Eligibility: Graduate Students
Instruction Method: In-person
Through an ongoing bi-campus collaboration (IIT-BME, with University of Chicago Medical Center Neurology and Neuroradiology), the student will be immediately embedded into a team of local experts in imaging, machine (ML), and neurology to improve the management of Multiple Sclerosis (MS). A faculty team guided summer project in Brain MRI data analysis and basic Machine Learning and Deep Learning Algorithm development through a hands-on approach.
Instructor: Mohammad Miralinaghi
Eligibility: Graduate Students
Instruction Method: In-person
Connected and autonomous vehicles show promise for increasing roadway safety and capacity. The goal of this course is to research transportation infrastructure design in the era of connected and autonomous vehicles. This includes implementing research in several areas, such as traffic signals, road design, and public transit and parking infrastructure. Through the coursework, the students will learn about cutting-edge aspects such as the impact of vehicle connectivity, and automation on our future roadway systems.
Instructor: Marcella Vaicik
Eligibility: 2-5 year eligible with the instructor’s approval
Instruction Method: In-person
This summer research course will engage students with drug delivery research. Students will make synthetic and natural biomaterials and conduct hands on experiments focused on quantitative evaluation of drug delivery from the designed vehicles. This course will be team based experiential learning led by biomaterials and drug delivery expert faculty in research laboratory setting.
Instructor: Jafar Saniie
Eligibility: 3-5 year eligible with the instructor’s approval
Instruction Method: Hybrid
Students in this course join the research team at the Embedded Computing and Signal Processing Research Laboratory (http://ecasp.ece.iit.edu/), where they will have the opportunity to design and learn basic topics in Internet of Things, Sensors, Signal Processing, Machine Vision, and Embedded Computing. This course focuses on applications of computer vision, deep learning, using embedded computing such as NVIDIA Jetson Nano and Raspberry Pi development boards.
Instructor: Francisco Ruiz
Eligibility: 2-5 year eligible with the instructor’s approval
Instruction Method: In-person
Several team projects:
-
- Adaptive Cycle Engnes, where compressions and expansions do not follow a fixed sequence, with air storage and energy recovery.
-
- Levitating land vehicles, designing and building single-seat prototype with induction-based magnetic levitation.
-
- EcoCar support, converting a 2023 Cadillac Lyriq electric vehicle to four-wheel drive.
Instructor: Scott Dawson
Eligibility: 3-5 year eligible with the instructor’s approval
Instruction Method: Hybrid
This project will study the behavior of unsteady aerodynamic systems, such as wings and fins that can pitch and flap, and that may also encounter strong gusts and currents. To understand and model these systems, we will combine aspects of classical theory with numerical simulations and experimental data.
Instructor: Scott Dawson
Eligibility: 3-5 year eligible with the instructor’s approval
Instruction Method: Hybrid
This project will focus on studying the properties of turbulence in fluid flows. In particular, students will implement algorithms to characterize and understand statistical structures in canonical turbulent flows. Students will learn to run computational fluid dynamics codes, and apply various data analysis methods. A background and interest in fluid mechanics, mathematics and programming is desirable.
Instructor: Abhinav Bhushan
Eligibility: 3-5 year eligible with the instructor’s approval
Instruction Method: Online
The goal of this project oriented class will be to learn about synthetic biology principles for applications in food and health. Students will undertake several small projects before taking on a larger project. Through the coursework, the students will learn about cutting-edge aspects such as CRISPR. Students will also be exposed to emerging trends in synthetic biology.
Instructor: Abhinav Bhushan
Eligibility: 3-5 year eligible with the instructor’s approval
Instruction Method: Online
Wearable devices have the potential of transforming health. Do you know how wearable devices work? The goal of this class will be to learn about technologies that could be used in wearable device and then develop a wearable device through a project. Some of the projects could move to prototyping. Healthcare remains the fastest growing sector in the US with a large number of engineers bring hired by medical device companies. Students will also be exposed to emerging trends in the larger medical device industry especially wearable and point of care devices.
Instructor: Keigo Kawaji
Eligibility: 3-5 year eligible with the instructor’s approval
Instruction Method: In-person
Through an ongoing bi-campus collaboration (IIT-BME, with University of Chicago Medical Center Neurology and Neuroradiology), the student will be immediately embedded into a team of local experts in imaging, machine (ML), and neurology to improve the management of Multiple Sclerosis (MS). A faculty team guided summer project in Brain MRI data analysis and basic Machine Learning and Deep Learning Algorithm development through a hands-on approach.
Instructor: Mehdi Modares
Eligibility: 2-5 year eligible with the instructor’s approval
Instruction Method: Online
The materials used in physical systems (structural, mechanical, and biomechanical) are susceptible to fracture under cyclic loading; this is known as fatigue failure. In this course, state of the art research and industry best practices will be used to investigate fatigue failure in various systems. This section proposes a research study on the state of the art and shortcomings of predictive models for fatigue of materials with anisotropic behavior. The areas identified for investigation include:
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- Understanding the basic fatigue phenomenon,
-
- Investigating fatigue models for anisotropic materials in Hybrid mechanical and biomechanical systems, and
-
- Investigating the techniques used for fatigue life prediction Hybrid historically and in current practice.
Instructor: Mohammad Miralinaghi
Eligibility: 2-5 year eligible with the instructor’s approval
Instruction Method: In-person
Connected and autonomous vehicles show promise for increasing roadway safety and capacity. The goal of this course is to research transportation infrastructure design in the era of connected and autonomous vehicles. This includes implementing research in several areas, such as traffic signals, road design, and public transit and parking infrastructure. Through the coursework, the students will learn about cutting-edge aspects such as the impact of vehicle connectivity, and automation on our future roadway systems.
Instructor: Marcella Vaicik Eligibility: 2-5 year eligible with the instructor’s approval Instruction Method: In-person This summer research course will engage students with drug delivery research. Students will make synthetic and natural biomaterials and conduct hands on experiments focused on quantitative evaluation of drug delivery from the designed vehicles. This course will be team based experiential learning led by biomaterials and drug delivery expert faculty in research laboratory setting.
For Graduate Students
Instructor: Jafar Saniie
Eligibility: Graduate Students
Instruction Method: Hybrid
Students in this course join the research team at the Embedded Computing and Signal processing Research Laboratory (http://ecasp.ece.iit.edu/), where they will have the opportunity to design and learn advanced topics in Internet of Things, Embedded Computing, Artificial Intelligence, and Deep Learning. This course focuses on practical applications of computer vision, deep learning, and wireless communications using smart phone, and embedded computing such as NVIDIA Jetson Nano, Raspberry Pi, and Xilinx PYNQ development boards.
Instructor: Francisco Ruiz
Eligibility: Graduate Students
Instruction Method: In-person
Several team projects:
-
- Adaptive Cycle Engnes, where compressions and expansions do not follow a fixed sequence, with air storage and energy recovery.
-
- Levitating land vehicles, designing and building single-seat prototype with induction-based magnetic levitation.
-
- EcoCar support, converting a 2023 Cadillac Lyriq electric vehicle to four-wheel drive.
Instructor: Scott Dawson
Eligibility: Graduate Students
Instruction Method: Hybrid
This project will study the behavior of unsteady aerodynamic systems, such as wings and fins that can pitch and flap, and that may also encounter strong gusts and currents. To understand and model these systems, we will combine aspects of classical theory with numerical simulations and experimental data.
Instructor: Scott Dawson
Eligibility: Graduate Students
Instruction Method: Hybrid
This project will focus on studying the properties of turbulence in fluid flows. In particular, students will implement algorithms to characterize and understand statistical structures in canonical turbulent flows. Students will learn to run computational fluid dynamics codes, and apply various data analysis methods. A background and interest in fluid mechanics, mathematics and programming is desirable.
Instructor: Abhinav Bhushan
Eligibility: Graduate Students
Instruction Method: Online
The goal of this project oriented class will be to learn about synthetic biology principles for applications in food and health. Students will undertake several small projects before taking on a larger project. Through the coursework, the students will learn about cutting-edge aspects such as CRISPR. Students will also be exposed to emerging trends in synthetic biology.
Instructor: Abhinav Bhushan
Eligibility: Graduate Students
Instruction Method: Online
Wearable devices have the potential of transforming health. Do you know how wearable devices work? The goal of this class will be to learn about technologies that could be used in wearable device and then develop a wearable device through a project. Some of the projects could move to prototyping. Healthcare remains the fastest growing sector in the US with a large number of engineers bring hired by medical device companies. Students will also be exposed to emerging trends in the larger medical device industry especially wearable and point of care devices.
Instructor: Keigo Kawaji
Eligibility: Graduate Students
Instruction Method: In-person
Through an ongoing bi-campus collaboration (IIT-BME, with University of Chicago Medical Center Neurology and Neuroradiology), the student will be immediately embedded into a team of local experts in imaging, machine (ML), and neurology to improve the management of Multiple Sclerosis (MS). A faculty team guided summer project in Brain MRI data analysis and basic Machine Learning and Deep Learning Algorithm development through a hands-on approach.
Instructor: Mohammad Miralinaghi
Eligibility: Graduate Students
Instruction Method: In-person
Connected and autonomous vehicles show promise for increasing roadway safety and capacity. The goal of this course is to research transportation infrastructure design in the era of connected and autonomous vehicles. This includes implementing research in several areas, such as traffic signals, road design, and public transit and parking infrastructure. Through the coursework, the students will learn about cutting-edge aspects such as the impact of vehicle connectivity, and automation on our future roadway systems.
Instructor: Marcella Vaicik
Eligibility: 2-5 year eligible with the instructor’s approval
Instruction Method: In-person
This summer research course will engage students with drug delivery research. Students will make synthetic and natural biomaterials and conduct hands on experiments focused on quantitative evaluation of drug delivery from the designed vehicles. This course will be team based experiential learning led by biomaterials and drug delivery expert faculty in research laboratory setting.
Experience Information
- Start Date
- May 23, 2023
- End Date
- Jul 15, 2023
- Apply By
- May 2, 2023
