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Job Details:

Job Description: 

We are offering an internship opportunity for PhD students to join our Anomalib RnD team, focusing on research and development in visual anomaly detection. The intern will play a crucial role in enhancing Anomalib by designing new algorithms and methodologies for detecting anomalies in visual data.

Key Responsibilities:

• Advanced Research: Conduct research to discover and refine novel approaches and techniques in visual anomaly detection. Keep abreast of the latest scientific advancements in machine learning, computer vision, and anomaly detection fields.
• Algorithm Design and Implementation: Develop and optimize state-of-the-art anomaly detection algorithms that enhance the capabilities of Anomalib. Ensure that these algorithms are efficient, scalable, and integrated seamlessly within the framework.
• Evaluation and Optimization: Systematically evaluate the performance of developed algorithms using diverse and complex datasets. Utilize feedback from these evaluations to make data-driven improvements.
• Cross-functional Collaboration: Work closely with both the research and development teams to align research findings with product development goals. Participate in discussions and workshops to share insights and collaboratively solve complex challenges.
• Scholarly Contribution: Document all phases of research and development comprehensively. Contribute to scientific papers, present findings at conferences, and participate in workshops relevant to the field.

Additional Information:

• Start date of the internship would be around May.
• The duration of the internship is typically 3 months, extendable based on project requirements and performance.
• The internship is remote.

Please submit your application, including a detailed CV, a cover letter highlighting your research interests and relevant experience, and links to publications or projects.
We value diversity and inclusion and encourage candidates from all backgrounds to apply. This is an opportunity to contribute to a leading-edge project in visual anomaly detection and to collaborate with experts in the field during your PhD studies.

Qualifications:

Minimum Qualifications:

• Education: Currently enrolled in a PhD program in Computer Science, Electrical Engineering, Applied Mathematics, or a related field, with a specific focus on machine learning, computer vision, and anomaly detection.

Desired Qualifications:

• Technical Expertise: Proficient in Python with experience using major machine learning and deep learning libraries (e.g., PyTorch and Lightning). Demonstrated ability in computer vision techniques and anomaly detection methodologies.
• Research Acumen: Proven track record of research in related areas, evidenced by publications in peer-reviewed journals or presentations at major conferences.
• Analytical Skills: exceptional problem-solving abilities, capable of working with complex data sets and extracting actionable insights.
• Communication and Collaboration: Strong written and verbal communication skills, with the ability to effectively document research and collaborate with a multidisciplinary team.

          

Job Type:

Student / Intern

Shift:

Shift 1 (United Kingdom)

Primary Location: 

Virtual United Kingdom

Additional Locations:

Business group:

The Network & Edge Group brings together our network connectivity and edge into a business unit chartered to drive technology end to end product leadership. It's leadership Ethernet, Switch, IPU, Photonics, Network and Edge portfolio is comprised of leadership products critically important to our customers.

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Work Model for this Role

This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on business need. This role may also be available as our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances the work model may change to accommodate business needs.

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