Postdoc Artificial Intelligence for the Dynamics of 2D Materials

27 Feb 2024
Job Information

Organisation/Company

Delft University of Technology (TU Delft)

Research Field

Technology

Researcher Profile

Recognised Researcher (R2)

Country

Netherlands

Application Deadline

15 Mar 2024 - 22:59 (UTC)

Type of Contract

Temporary

Job Status

Not Applicable

Hours Per Week

40.0

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

We live in an era where artificial intelligence (AI) stands as a beacon of innovation, where advances in machine learning (ML) profoundly impact many aspects of our society. Nevertheless, the use of ML in engineering is still in its infancy. Many areas of engineering that could leverage on ML include (meta)material characterization and design, computational structural design, and symbolic regression (i.e., obtaining mathematical expressions from experimental data), to name a few. At Delft University of Technology we recognize the immense potential AI holds in revolutionizing a broad range of engineering problems.

In this project we will look at ultra-thin materials, which are at the forefront of technological development due to their extraordinary properties. Graphene, for instance, stands as the strongest, most impermeable, and conductive material known to date. There is a myriad of applications where graphene could find its way. However, the materials’ extreme noise sensitivity gives rise to a plethora of poorly-understood phenomena. Through the use of ML, we will derive precise mathematical expressions that describe the behavior of these materials in the presence of noise, paving the way for unleashing their full potential for extreme sensing in high-tech industries such as aerospace and medical.

As a postdoctoral researcher your tasks will include:

• Developing on "deep symbolic regression", i.e., an existing ML framework that uses neural networks to determine mathematical expressions from experimental or numerical data.
• Coordinate the work of related MSc projects.
• Help with writing proposals to secure further funding for this topic.
• Publishing in renowned journals, and presenting your research at international meetings.

Requirements
Specific Requirements

You should have the following qualifications:

• A strong background in machine learning.
• Knowledge of Bayesian optimization, Gaussian processes is a plus.
• Background in mechanics is highly desired.
• A PhD degree in computer science, applied mathematics, or engineering (mechanical, civil, aerospace, etc.).
• High motivation for teamwork and excellent communication skills.

Additional Information
Benefits

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities. The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

This position is a temporary assignment for 12 months.

Selection process

A pre-employment screening can be part of the selection procedure.

Additional comments

For information about the application procedure, please contact Linda Verhaar, [email protected] .

For more information about this vacancy, please contact Dr. Alejandro M. Aragón, phone: +31 (0)15 278 22 67, e-mail: [email protected] .

Are you interested in this vacancy? Please apply no later than 15 March 2024 via the application button and upload your motivation and CV.

• You can apply online. We will not process applications sent by email and/or post.
• Please do not contact us for unsolicited services.

Website for additional job details

https://www.academictransfer.com/338275/

Work Location(s)

Number of offers available

1

Company/Institute

Delft University of Technology

Country

Netherlands

City

Delft

Postal Code

2628 CD

Street

Mekelweg 2

Geofield

Where to apply

Website

https://www.academictransfer.com/en/338275/postdoc-artificial-intelligence-for-…

Contact

City

Delft

Website

http://www.tudelft.nl/

Street

Mekelweg 2

Postal Code

2628 CD

STATUS: EXPIRED