Master Thesis / Internship - Deep learning with layer-local objectives

21 Feb 2024
Job Information

Organisation/Company

Forschungszentrum Jülich

Research Field

Computer science

Researcher Profile

Recognised Researcher (R2)

Country

Germany

Application Deadline

21 Mar 2024 - 00:00 (UTC)

Type of Contract

To be defined

Job Status

Full-time

Hours Per Week

To be defined

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Your Job:

In this project, we'll probe representations in hidden layers of pretained networks, specifically ViT- and ConvNext-type architectures, to reverse engineer the types of objectives that might optimally lead to these representation. We then aim to understand whether the obtained objectives, applied in a layer-local manner, lead to performant deep network learning.

Your tasks in detail:

• Implement a flexible data loading framework capable of incorporating multiple datasets
• Develop strategy to decode image-to-image as well as image-to-label tasks from hidden representations
• Train decoders for multiple datasets / datatypes on the representations in hidden layers of pretrained networks (ViT, ConvNext)
• Implement search for adapted, layer-local objectives to train deep networks, based on decoder performance

Your Profile:

• Current master studies in biomedical engineering, physics, computer science, mathematics, electrical/electronic engineering or in a related field
• Strong programming skills (Python)
• Familiarity with machine learning and deep learning frameworks (e.g., PyTorch)
• Experience with HPC systems is a plus
• Ability to work independently and as part of a team
• Experimental enthusiasm is a must!

Please feel free to apply for the position even if you do not have all the required skills and knowledge. Missing skills can be learned.

Our Offer:

We work on the very latest issues that impact our society and are offering you the chance to actively help in shaping the change! We support you in your work with:

• An interesting and socially relevant topic for your thesis with future-oriented themes
• Ideal conditions for gaining practical experience alongside your studies
• An interdisciplinary collaboration on projects in an international, committed and collegial team
• Excellent technical equipment and the newest technology
• Qualified support through your scientific colleagues
• The chance to independently prepare and work on your tasks
• Flexible work (location) arrangements, e.g. remote work
• Flexible working hours as well as a reasonable remuneration

In addition to exciting tasks and a collaborative working atmosphere at Jülich, we have a lot more to offer: https://go.fzj.de/benefits

Place of employment: Aachen

We welcome applications from people with diverse backgrounds, e.g. in terms of age, gender, disability, sexual orientation / identity, and social, ethnic and religious origin. A diverse and inclusive working environment with equal opportunities in which everyone can realize their potential is important to us.

Requirements
Additional Information
Work Location(s)

Number of offers available

1

Company/Institute

Forschungszentrum Jülich

Country

Germany

Geofield

Where to apply

Website

https://illbeback.ai/job/master-thesis-internship-deep-learning-with-layer-loca…

STATUS: EXPIRED