Cybersecurity Research Intern
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Job Location:
Monthly Salary:
Posted on:
30+ days ago
Vacancies:
1 Vacancy
Job Summary
This internship offers a unique opportunity to work at the intersection of cybersecurity healthcare and medical device security contributing to advanced research in a collaborative international environment. This intern will be paid a monthly allowance of $1500.
Key Responsibilities
- Assist in the development and implementation of a test network for medical devices
- Use industry-standard network profiling tools at the packet & flow levels
- Document research findings and contribute to presentations and publications
Requirements
- Education: pursuing or completed Bachelors degree in Computer Science Computer Engineering Informatics or a related field; preferably from a reputable university.
- Background: Strong foundation in networking and cybersecurity concepts.
- Programming: Proficiency in Python comfortable with libraries such as Scapy
- Self-Learning & Problem-Solving: We do not teach coding; candidates must be either capable of solving complex programming/software engineering tasks or should be able and willing to independently learn and apply advanced programming skills to complete these tasks.
- Experience: At least one of the following:
oPython networking
operforming security tasks (such as pen-testing) on the network
ousing LLMs to solve problems (e.g. prompt engineering)
- Desirable Skills:
oKnowledge of networking tools such as Wireshark or tcpdump.
o Web development (front-end or back-end) experience.
oProficiency in the Go programming language.
oExperience working with IoT devices
oExperience using Docker and Kubernetes
Duration: 6 9 months
Informal enquiries are greatly welcome and can be directed to Dr Viktor Schlegel
Questions about the recruitment process should go to the HR at Imperial Global Singapore
Required Experience:
Intern
Key Skills
- Robotics
- Machine Learning
- Python
- AI
- C/C++
- Data Collection
- Research Experience
- Signal Processing
- Natural Language Processing
- Computer Vision
- Deep Learning
- Tensorflow
