Is the internship at University of Bayreuth paid and what compensation can candidates expect?

Yes, this is a paid internship opportunity. Exact compensation details are typically finalized during the company interview process.

What are the working arrangements and expected duration for the Fixed-Term Research Associate (m/f/d, E13 TV-L, 67%) in the field of Machine Learning for Materials Chemistry role?

This position is a not disclosed, structured as a not disclosed role.

Does University of Bayreuth provide visa sponsorship or assistance for international applicants?

Regarding visa assistance and sponsorship for this role, the official status is currently listed as: Not disclosed.

Fixed-Term Research Associate (m/f/d, E13 TV-L, 67%) in the field of Machine Learning for Materials Chemistry

Q: How do I apply for the Fixed-Term Research Associate (m/f/d, E13 TV-L, 67%) in the field of Machine Learning for Materials Chemistry position at University of Bayreuth and track my application?

We provide a direct link to the official application portal. You can apply directly here . Additionally, you can easily track this and other applications by clicking the bookmark icon to save it to your YGI Kanban board .

Q: What educational background or degree level is required for the Fixed-Term Research Associate (m/f/d, E13 TV-L, 67%) in the field of Machine Learning for Materials Chemistry at University of Bayreuth?

Candidates are generally expected to be holding or pursuing the following degree levels: Master.

University of Bayreuth

Found 4 weeks ago

Location

Bayreuth, Germany

Time

Not disclosed

Work Mode

Not disclosed

Salary

E13 TV-L

Visa Help

Not disclosed

Last Verified

4 weeks ago

Education

Master

Skills & Qualifications

Technical Skills

machine learning
artificial intelligence
atomistic simulations
deep learning techniques

Soft Skills

Strong communication skills
curious mind

Job Description

Develop novel machine learning (ML) and artificial intelligence (AI) methods to describe the properties of complex and disordered materials. Perform and analyze complex atomistic simulations of functional energy materials under realistic operating conditions. Collaborate with experimental partners on cutting edge materials chemistry research for batteries, catalysis and beyond.

Requirements

Completed university degree (Master's or Diploma) in Chemistry, Physics, Computer Science or related fields.
Background in atomistic scale simulations and/or deep learning techniques.
Interest in pushing the boundaries of materials simulations.
Strong communication skills and a curious mind.

AI & Machine Learning

Applied Machine Learning

Languages

German

Apply Now

11Open Positions

Open Positions

11Open Positions

Fixed-Term Research Associate (m/f/d, E13 TV-L, 67%) in the field of Machine Learning for Materials Chemistry

Education

Skills & Qualifications

Technical Skills

Soft Skills

Job Description

Requirements

Related Field

Related Subfield

Languages